2024-03-28T16:41:34Z
https://ejournal.undip.ac.id/index.php/index/oai
oai:ojs.ejournal.undip.ac.id:article/4832
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
ANALISIS PENGARUH KARAKTERISTIK WILAYAH (KELURAHAN) TERHADAP BANYAKNYA KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA SEMARANG
Rahmawati, Rita
Kartono, Kartono
Sulistyo, Robertus Heri
Noranita, Betha
Sarwoko, Eko Adi
Wardaya, Asep Yoyo
DBD still become one of the major problems of public health in Indonesia because the death rate tended sufferers to increase from year to year. Incredible happening (KLB) of DBD which was initially occurring every five years, now it’s getting often happens. In the city of Semarang, during 2009 occurring 165 times KLB in urban village, 35 times KLB in the level of community health centers and 15 times KLB at the district level. Though the number of DBD cases in 2009 from 2008 was declining, but in this year also noted that the number of deaths resulting from DBD increased to 43 people from 18 people in 2008. This research aims to analyze the characteristics of the neighborhood (whose data is always updated by BPS via PODES) that affect the number of cases of DBD (whose data is always updated by DKK) in Semarang city, by creating the best regression models using stepwise technique. Regression model analysis of results obtained best is Y = 23.029 + 0.004 X1 – 0.074 X2 + 0.070X3, where Y is IR/10000 PDDK, that is the number of residents affected by DBD for each 10000 inhabitants, X1 is the number of residents aged 15-24 years, X2 is total area of land of rice fields and X3 is area of land for buildings and grounds around the page.
Keywords: DBD, Characteristics of the Neighborhood, Regression, Stepwise
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4832
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/32951
2023-11-25T03:05:19Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA
Kartikasari, Puspita
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University https://scholar.google.com/citations?user=0rmtVrUAAAAJ&hl=en https://orcid.org/0000-0001-9571-5858
Yasin, Hasbi
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University https://orcid.org/0000-0002-4887-9646
Maruddani, Di Asih I
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University
ARFIMA; Prediction; COVID-19
Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also simpler. For cases of recovery and death, the number is increasing even though the case of death is still very high compared to cases of recovery.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/32951
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8287
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
IDENTIFIKASI AUTOKORELASI SPASIAL PADA JUMLAHPENGANGGURAN DI JAWA TENGAH MENGGUNAKAN INDEKS MORAN
Wuryandari, Triastuti
Hoyyi, Abdul
Kusumawardani, Dewi Setya
Rahmawati, Dwi
Unemployment is caused by the work force or job seekers are not proportional with the number of existing jobs. Unemployment is often a problem in the interconnected economy due to unemployment, productivity and income will be reduced. The number of unemployed in an are a expected to be affected by unemployment in the surrounding area. This is made possible because of the proximity factor or adjacency between regions, it is estimated that there are linkages to the regional unemployment rate. To determine the relationship between regional linkages used Moran’s Index method. The number of unemployed in Central Java, obtained Moran’s Index value = 0.0614. Moran's Index values in the range 0 < I ≤ 1 indicating the presence of spatial autocorrelation is positive but small correlation can be said because of near zero, orit can be concluded that the similarity between the district does not have a value or indicate that unemployment among districts in Central Java has a small correlation.
Keywords: Unemployment, Moran’s Index, Central Java, Autocorrelation, Spatial
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8287
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/38206
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
THIRD MOLAR MATURITY INDEX IN INDONESIAN JUVENILES: COMPARING LINEAR AND POLYNOMIAL KERNEL PERFORMANCE IN SUPPORT VECTOR REGRESSION FOR DENTAL AGE ESTIMATION
Boedi, Rizky Merdietio
Department of Dentistry, Universitas Diponegoro https://orcid.org/0000-0002-5045-6773
Saputri, Rosalina Intan
Faculty of Dentistry, Maranatha Christian University https://orcid.org/0000-0003-0811-6270
Forensic Dentistry; Dental Age Estimation;Third Molar; Third Molar Maturity Index; Support Vector Regression,;Polynomial Kernel
Dental age estimation is a branch of forensic odontology that plays a pivotal role in identifying, examining, or determining the legal status of the living and the dead. This research explores the capability of support vector regression to estimate chronological age from the third molar maturity index (I3M) in Indonesian Juveniles and compares the linear and kernel performance. Two hundred and twenty-two orthopantomo-graphy were measured using I3M in the lower left third molar and processed using R Studio with Caret extension. The analysis was separated into two groups, group 1 using only I3M as a predictor, and group 2 using both I3M and sex. Both groups were analyzed using SVR with the linear and polynomial kernel. The result suggests that using polynomial kernel SVR in group 1 produces the best results, with an R2 value of 0.64, RMSE of 1.588 years, and MAE of 1.25 years using degree = 3, c = 0.25. However, the addition of a sex predictor in the model reduces its accuracy when using the polynomial kernel.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/38206
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/38206/148798
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9202
2018-02-27T10:16:02Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA
Saptyani, Marita
Jurusan Statistika, FMIPA, Universitas Sebelas Maret
Sulandari, Winita
Jurusan Statistika, FMIPA, Universitas Sebelas Maret
Pangadi, Pangadi
Jurusan Statistika, FMIPA, Universitas Sebelas Maret
Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecast 336 next period.
Keywords: DSARIMA, MAPE, Electricity, Bawen
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9202
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/51566
2024-02-26T03:51:54Z
media_statistika:ART
nmb a2200000Iu 4500
"240226 2024 eng "
2477-0647
1979-3693
dc
MODELING OF WORLD CRUDE OIL PRICE BASED ON PULSE FUNCTION INTERVENTION ANALYSIS APPROACH
Aliffia, Netha
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115 https://orcid.org/0000-0001-5506-0121
Sediono, Sediono
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
Suliyanto, Suliyanto
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
Mardianto, M. Fariz Fadillah
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
Amelia, Dita
Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
Crude Oil Price; Intervention Analysis; Pulse Function; Russia-Ukraine Geopolitical Conflict
Crude oil has important role in global economy, including Indonesia with considerable dependence on crude oil energy consumption. The increase in crude oil prices can be triggered by several factors, one of which is geopolitical conflict that occurred due to Russia's invasion of Ukraine on February 24, 2022. As the result, world crude oil prices rose above US$100 per barrel for the first time since 2014. Therefore, this study uses pulse function intervention analysis approach to evaluate the impact of certain events in predicting data over the next few periods. The pulse function is used because the intervention occurs at the moment t only. The data used starts from June 8, 2020 to September 19, 2022 on weekly basis with the proportion of training and testing data is 90:10. The best intervention model obtained is ARIMA (3,2,0) with b=0, s=1, r=2, and intervention point at T=91. The prediction results for the next 12 periods obtained MAPE value of 2.8982% and MSE of 10.2687. This study is expected to help reduce risks due to uncertainty in world crude oil prices and in line with the goals of the Sustainable Development Goals (SDGs) to ensure access to reliable, sustainable, and affordable energy.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-12-22 11:43:06
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/51566
MEDIA STATISTIKA; Vol 16, No 2 (2023): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/51566/164676
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/47474
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
MANAGING HEART RELATED DISEASE RISKS IN BPJS KESEHATAN USING COLLECTIVE RISK MODELS
Yogesswara, Gede Ary Prabha
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia, 55281
Qoyyimi, Danang Teguh
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia, 55281
Abdurakhman, Abdurakhman
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia, 55281
BPJS Kesehatan; Collective risk model; Heart disease; Lognormal distribution; Poisson distribution
BPJS Kesehatan is a legal entity established to administer the health service program using the insurance system. Heart related diseases is a disease with the largest coverage cost in Indonesia. It can be calculated by using the collective risk model as an approximation of the aggregate loss model. This model is a compound distribution from claim frequency and claim severity, where claim frequency be the primary distributions. The Poisson distribution can be used to the distribution of the heart disease claim frequency. Whereas, the distribution of the heart disease claim severity has a lognormal distribution. The model obtained can explain the aggregate loss of heart disease claims properly.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/47474
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/47474/170374
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13130
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
PEMODELAN DATA KEMATIAN BAYI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION
Ramadhan, Riza F.
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik
Kurniawan, Robert
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik
Overdispersion phenomenon and the influence of location or spatial aspect on data are handled using Binomial Geographically Weighted Regression (GWNBR). GWNBR is the best solution to form a regression analysis that is specific to each observation’s location. The analysis resulted in parameter value which different from one observation to another between location. The Weighting Matrix Selection is done before doing The GWNBR modeling. Different weighting will resulted in different model. Thus this study aims to investigate the best fit model using infant mortality data that is produced by some kind of weighting such as fixed kernel Gaussian, fixed kernel Bisquare, adaptive kernel Gaussian and adaptive kernal Bisquare in GWNBR modeling. This region study covers all the districts/municipalities in Java because the number of observations are more numerous and have more diverse characteristics. The study shows that out of four kernel functions, infant mortality data in Java2012, the best fit model is produced by fixed kernel Gaussian function. Besides that GWNBR with fixed kernel Gaussian also shows better result than the poisson regression and negative binomial regression for data modeling on infant mortality based on the value of AIC and Deviance.
Keywords: GWNBR, infant mortality, fixed gaussian, fixed bisquare, adaptive gaussian, adaptive bisquare.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13130
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18383
2023-12-12T02:27:03Z
media_statistika:FMT
nmb a2200000Iu 4500
"171228 2017 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Vol. 10 No. 2 Juni 2017
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18383
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2496
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
ANALISIS DATA PANEL UNTUK MENGUJI PENGARUH RISIKO TERHADAP RETURN SAHAM SEKTOR FARMASI DENGAN LEAST SQUARE DUMMY VARIABLE
Astuti, Tutut Dewi
Maruddani, Di Asih I
Panel data analysis is a method of studying pooling observations on a cross-section of subjects over several time periods. There are several types of panel data analytic models, constant coefficients models, fixed effects models, and random effects models. Fixed effects models would have constant slopes but intercepts that differ according to the cross-sectional (group) unit. While the intercept is cross-section (group) specific, it may or may not differ over time. To show how to test for the presence of statistically significant group and/or time effects, i-1 dummy variables are used to designate the particular group, so we use Least Squares Dummy Variable method. In this paper, we use this method for testing the relationship between risk and stock return at farmation sector data in Indonesia for the time period 2007-2008. The empirical results showed that the model is statistically significant time effects.
Keywords : Risk, Stock Return, Panel Data, Least Square Dummy Variable
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2496
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18155
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB
Shina, Arya Fendha Ibnu
Institut Agama Islam Negeri (IAIN) Salatiga
Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data.
Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18155
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2513
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
BAGGING REGRESI LOGISTIK ORDINAL PADA STATUS GIZI BALITA
Akbar, Muhammad Sjahid
Mukarromah, Adatul
Paramita, Lalita
World Health Organization-National Centre for Health Statistic (WHO-NCHS) is standart nutritional status used in Indonesia, it based on Kartu Menuju Sehat (KMS). These Indices can be expressed in terms of Z-score based Weight-for-Age. This Indices need comparison considering the fact which cause nutritional status not only Weight-for-Age. The aim from this research to obtain bagging ordinal logistics regression for WHO-NCHS nutritional status and new nutritional status. A new nutritional status expressed in terms of cluster, while classification function expressed from logit model of ordinal logistics regression. The result for new nutritional status bagging obtained at 60 bootstrap replicated that is 76.345%, this model can decrease misclassification until 22.046%. While bagging for WHO-NCHS nutritional status can increase accurate classification from single data set 75.863% at 150 bootstrap replicated.
Keywords: Child nutritional status, Bagging, Ordinal logistics regression.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2513
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/24668
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL
Hakim, Arief Rachman
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro http://orcid.org/0000-0002-4887-9646
Rusgiyono, Agus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Life Expectancy; SDM; Spatial Regression
Central Java Province in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). the difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y), but the HR method, the effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. The selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24668
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2632
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
PEMODELAN GENERAL REGRESSION NEURAL NETWORK UNTUK PREDIKSI TINGKAT PENCEMARAN UDARA KOTA SEMARANG
Warsito, Budi
Rusgiyono, Agus
Amirillah, M. Afif
This paper is discuss about General Regression Neural Network (GRNN) modelling to predict time series data, i.e. the air pollution rate in Semarang City comprises the floating dust, carbon monoxide (CO) and nitrogen monoxide (NO). The GRNN model have four processing layer that are input layer, pattern layer, summation layer and output layer. The input variable is determined by the ARIMA model. The result of GRNN modelling shows that the network have a good performance both at predict in sample and predict out of sample, that can be seen from the mean square error.
Keywords: GRNN, predict, air pollution
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2632
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20101
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
SPILLOVER EFFECT INFLASI DAGING SAPI ANTAR KOTA: APLIKASI METODE BEKK-GARCH UNTUK JAKARTA, SALATIGA, DAN SURABAYA
Wahyuni, Ribut Nurul Tri
Politeknik Statistika STIS
Nasrudin, Nasrudin
Politeknik Statistika STIS
Beef Inflation; Spillover Effect; BEKK-GARCH
Beef consumption in Indonesia tends to increase and its price fluctuates. In addition to internal factors, the volatility of beef inflation can also be influenced by other regions (spillover effect). Using BEKK-GARCH model, we try to show spillover effect the volatility of beef inflation in Jakarta, Salatiga, and Surabaya. The transmissions of news effects occur from Jakarta and Surabaya to Salatiga and from Jakarta and Salatiga to Surabaya. Transmission of two-way volatility occurs between Jakarta and Surabaya. Furthermore, the transmission of one-way volatility occurrs from Jakarta to Salatiga. Price fluctuation in consumer areas will be followed by price fluctuation in other consumer areas and producer areas. Therefore, controlling beef inflation should be began from consumer areas.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20101
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/20101/52569
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/20101/91193
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/36773
2022-07-27T00:34:53Z
media_statistika:FMT
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
Front Matter Vol. 11 No. 2 2018
Statistika, Media
Cover dan Daftar Isi Media Statistika Vol. 11 No. 2 Desember 2018
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36773
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7639
2016-03-15T17:19:31Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG
Anggraeni, Dian
Prahutama, Alan
Andari, Shofi
Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834.
Key words: GSTAR, Vehicle Volume, Space-Time Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/39579
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
RELATIVE RISK OF CORONAVIRUS DISEASE (COVID-19) IN SOUTH SULAWESI PROVINCE, INDONESIA: BAYESIAN SPATIAL MODELING
Aswi, Aswi
Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar http://orcid.org/0000-0002-0639-2936
Mauliyana, Andi
Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar
Tiro, Muhammad Arif
Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar
Bustan, Muhammad Nadjib
Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar
COVID-19; Relative risk; Bayesian CAR localised
The Covid-19 has exploded in the world since late 2019. South Sulawesi Province has the highest number of Covid-19 cases outside Java Island in Indonesia. This paper aims to determine the most suitable Bayesian spatial conditional autoregressive (CAR) localised models in modeling the relative risk (RR) of Covid-19 in South Sulawesi Province, Indonesia. Bayesian spatial CAR localised models with different hyperpriors were performed adopting a Poisson distribution for the confirmed Covid-19 counts to examine the grouping of Covid-19 cases. All confirmed cases of Covid-19 (19 March 2020-18 February 2021) for each district were included. Overall, Bayesian CAR localised model with G = 5 with a hyperprior IG (1, 0.1) is the preferred model to estimate the RR based on the two criteria used. Makassar and Toraja Utara have the highest and the lowest RR, respectively. The group formed in the localised model is influenced by the magnitude of the mean and variance in the count data between areas. Using suitable Bayesian spatial CAR localised models enables the identification of high-risk areas of Covid-19 cases. This localised model could be applied in other case studies.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/39579
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8494
2018-02-27T11:04:04Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
PERBANDINGAN KLASIFIKASI NASABAH KREDIT MENGGUNAKAN REGRESI LOGISTIK BINER DAN CART (CLASSIFICATION AND REGRESSION TREES)
Waluyo, Agung
Jurusan Statistika, FSM, Universitas Diponegoro
Mukid, Moch. Abdul
Jurusan Statistika, FSM, Universitas Diponegoro
Wuryandari, Triastuti
Jurusan Statistika, FSM, Universitas Diponegoro
Credit is the greatest asset managed the bank and also the most dominant contributor to the bank’s revenue. Debtors to pay their credit to the bank may smoothly or jammed. This study aims to identify the variables that affect a debtor’s credit status and compare the acuration of classification method both classification and regression trees (CART) and logistic regression. The variables used were debtor’s gender, education level, occupation, marital status, and income. By using logistic regression, it was known that only the debtor’s income effect their credit status with the classification accuration reach into 80%. By using CART, there were some variables affect the credit status and the classification accuration 80,9%. This paper showed that the performance of CART in classifying the credit status of debtors was better than logistic regression.
Keywords: Credit Status, Logistic Regression, CART
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8494
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/59851
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231207 2023 eng "
2477-0647
1979-3693
dc
SIMULATION STUDY FOR UNDERSTANDING THE PERFORMANCE OF PARTIAL LEAST SQUARES–MODIFIED FUZZY CLUSTERING (PLSMFC) IN FINDING GROUPS UNDER STRUCTURAL EQUATION MODEL
Mukid, Moch. Abdul
Department of Statistics, Universitas Diponegoro
Otok, Bambang Widjanarko
Department of Statistics, Institut Teknologi Sepuluh Nopember
Suparti, Suparti
Department of Statistics, Universitas Diponegoro
Unobserved Heterogenity; Partial Least Squares; Fuzzy Clustering
In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/59851
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/46412
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT
Siswantining, Titin
Departemen Matematika, Universitas Indonesia https://scholar.google.co.id/citations?hl=id&user=dyUfiXMAAAAJ https://orcid.org/0000-0001-5160-0020
Pratama, Stanley
Department of Mathematics, Universitas Indonesia
Sarwinda, Devvi
Department of Mathematics, Universitas Indonesia https://orcid.org/0000-0001-8644-2560
natural language processing; natural language sentence matching; recurrent neural network
Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/46412
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11722
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
PENGELOMPOKAN KABUPATEN/KOTA BERDASARKAN KOMODITAS PERTANIAN MENGGUNAKAN METODE K MEDOIDS
Wuryandari, Triastuti
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Rusgiyono, Agus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Setyowati, Etik
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The land in Central Java have a lot of nutrients, so considered suitable for agriculture. North Central Java and some areas in Central Java suitable agriculture for food crops of rice and other crops such as corn, soybeans, peanuts, sweet potatoes and cassava. With the diversity of agricultural production of food crops in Central Java it is necessary to facilitate the grouping of government in determining the specific policy in agriculture in order to achieve national food security. These grouping using cluster analysis with non hierarchical partitioning methode k medoids. The cluster using a point value from the agricultural commodity crops, thereby reducing the sensitivity of the data outliers.
Keywords: Central Java, Agricultural Commodities, Cluster Analysis, Non-Hierarchical, k Medoids, Outlier
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11722
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018
oai:ojs.ejournal.undip.ac.id:article/16298
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
ANALISIS PENGARUH LOKASI DAN KARAKTERISTIK KONSUMEN DALAM MEMILIH MINIMARKET DENGAN METODE REGRESI LOGISTIK DAN CART
Bekti, Rokhana Dwi
Jurusan Statistika, Fakultas Sains Terapan, Institut Sains & Teknologi AKPRIND Yogyakarta
Pratiwi, Noviana
Jurusan Statistika, Fakultas Sains Terapan, Institut Sains & Teknologi AKPRIND Yogyakarta
Jatipaningrum, Maria Titah
Jurusan Statistika, Fakultas Sains Terapan, Institut Sains & Teknologi AKPRIND Yogyakarta
Auliana, Dina
Jurusan Statistika, Fakultas Sains Terapan, Institut Sains & Teknologi AKPRIND Yogyakarta
Konsumen saat ini memiliki banyak pilihan untuk berbelanja memenuhi kebutuhan sehari-hari, baik di pasar modern maupun tradisional, serta ritel khususnya dalam bentuk minimarket. Dengan demikian persaingan antar minimarket juga sangat tinggi. Setiap minimarket memiliki strategi pemasaran yang berbeda-beda, karena karakteristik konsumen dalam berbelanja juga berbeda-beda. Dalam strategi pemasaran, informasi dari berbagai aspek, yaitu dapat dari segi konsumen, pasar, pesaing, maupun produk sangat diperlukan. Pada penelitian ini melakukan analisis factor yang berpengaruh pada minat konsumen yang berbelanja di minimarket. Factor yang dikaji adalah dari segi konsumen, baik karakteristik maupun lokasi tempat tinggal. Data yang digunakan adalah data primer dengan melakukan survey wawancara pada konsumen di Kecamatan Ngaglik, Kab. Sleman, DIY. Sedangkan sampel minimarket adalah Indomaret. Metode analisis yang digunakan adalah regresi logistik dan Classification and Regression Trees (CART). Hasil analisis menunjukkan bahwa faktor-faktor yang signifikan berpengaruh terhadap minat belanja di Indomaret dengan metode regresi logistik adalah variabel jenis kelamin, pengeluaran rata-rata perbulan, dan lokasi tempat tinggal konsumen. Sedangkan factor berperan penting dalam pembentukan pohon klasifikasi CART adalah juga lokasi. Apabila dibandingkan berdasarkan nilai ketepatan klasifikasi, metode CART, sebagai metode nonparametrik yang tidak memiliki asumsi distribusi tertentu, menghasilkan ketepatan klasifikasi yang lebih tinggi dibandingkan regresi logistik.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/16298
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2480
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
MODEL PENYUSUTAN DARAB JUMLAH PESERTA ASURANSI PADA ASURANSI JIWA
Sunarsih, Sunarsih
Sakinata, Meidar
Multiple decrement model in life insurance is a decrement model where the decrement of amount participants of insurance do not only because of just one cause of decrement but because of two or more causes of decrement, so that can provide various benefit in one policy of insurance program. In this paper, using two causes of decrement, that is disability and death. In construction of a multiple-decrement table, can be associated from the tables of single-decrement which have known. The number of premium payments for life insurance depends on what kind of insurance program that have been taken. A life insurance, the number of premium depends on of age, even though on term insurance, except age is policy time period.
Keywords: Insurance, Multiple Decrement Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2480
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/19797
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
Tarno, Tarno
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro https://scholar.google.co.id/citations?user=rSe3L94AAAAJ&hl=id
Rusgiyono, Agus
Warsito, Budi
Sudarno, Sudarno
Ispriyanti, Dwi
The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.
Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/19797
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
ind
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2508
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
CREDIT SPREADS PADA REDUCED-FORM MODEL
Maruddani, Di Asih I
Rosadi, Dedi
Gunardi, Gunardi
Abdurakhman, Abdurakhman
There are two primary types of models in the literature that attempt to describe default processes for debt obligations and other defaultable financial instruments, usually referred to as structural and reduced-form (or intensity) models. Structural models use the evolution of firms’ structural variables, such as asset and debt values, to determine the time of default. Reduced form models do not consider the relation between default and firm value in an explicit manner. Reduced form models assume that the modeler has the same information set as the market - incomplete knowledge of the firm’s condition. that leads to an inaccessible default time. The key distinction between structural and reduced form models is not whether the default time is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology. Credit spreads are used to measure credit premium, which compensates risk-averse investors for assuming
credit risk. Therefore, the credit spreads should remain positive. The higher credit risk assumed by the investors, the higher credit premium got be payed by them. In this paper, we have to to determine the credit spreads of reduced-form model.
Keywords: Reduced-Form Model, Hazard Rate, Credit Spreads
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2508
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22427
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
PERHITUNGAN VALUE AT RISK DENGAN PENDEKATAN THRESHOLD AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY-GENERALIZED EXTREME VALUE
Tyas, Mutik Dian Prabaning
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Maruddani, Di Asih I
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Rahmawati, Rita
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Stock is the most popular type of financial asset investment. Before buying a stock, an investor must estimate the risks which will be received. Value at Risk (VaR) is one of the methods that can be used to measure the level of risk. When investing in stock, if an investor wants to earn high returns, then he must be prepared to face higher risks. Most of stock return data have volatility clustering characteristic or there are cases of heteroscedasticity and the distribution of stock returns has heavy tail. One of the time series models that can be used to overcome the problem of heteroscedasticity is the ARCH/GARCH model, while the method for analyzing heavy tail data is Extreme Value Theory (EVT). In this study used an asymmetrical ARCH model with the Threshold ARCH (TARCH) and EVT methods with Generalized Extreme Value (GEV) to calculate VaR of the stock return from PT Bumi Serpong Damai Tbk for the period of September 2012 to October 2018. The best chosen model is AR([3])–TARCH(1). At the 95% confidence level, the maximum loss an investor will be received within the next day by using the TARCH-GEV calculation is 0.18%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22427
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2627
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
PEMILIHAN PARAMETER THRESHOLD OPTIMAL DALAM ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN PROSEDUR FALSE DISCOVERY RATE (FDR)
Suparti, Suparti
Tarno, Tarno
Haryono, Yon
If X is predictor variable and Y is response variable of following model Y = f (X) +e with function f is regression which not yet been known and e is independent random variable with mean 0 and variant , hence function of f can estimate with parametric and nonparametric approach. At this paper estimate f with nonparametric approach. Nonparametric approach that used is wavelet shrinkage or wavelet thresholding method. At function estimation with method of wavelet thresholding, what most dominant determine level of smoothing estimator is value of threshold. The small threshold give function estimation very no smoothly, while the big value of threshold give function estimation very smoothly. Therefore require to be selected value of optimal threshold to determine optimal function estimation.
One of the method to determine the value of optimal threshold is with procedure of False Discovery Rate ( FDR). In procedure of FDR, the optimal threshold determined by selection of level of significance. Smaller mount used significance progressively smoothly its .
Keywords: Nonparametric regression, wavelet thresholding estimator, procedure of False Discovery Rate
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2627
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/27941
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
PERSAMAAN DIFERENSIAL ORNSTEIN-UHLENBECK DALAM PERAMALAN HARGA SAHAM
Hidayat, Amam Taufiq
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Subanar, Subanar
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Levy Process; Ornstein-Uhlenbeck process; BNS Gamma Ornstein Uhlenbeck Model
Geometric Brownian motion is one of the most widely used stock price model. One of the assumptions that is filled with stock return volatility is constant. Gamma Ornstein-Uhlenbeck process a model to describe volatility in finance. Additionally, Gamma Ornstein-Uhlenbeck process driven by Background Driving Levy Process (BDLP) compound Poisson process and the marginal law of volatility follows a Gamma distribution. Barndorff-Nielsen and Shepard (BNS) Gamma Ornstein-Uhlenbeck model can to sample the process for the stock price with volatility follows Gamma Ornstein-Uhlenbeck process. Based on these, the simulation result are compared BNS Gamma Ornstein-Uhlenbeck model with geometric Brown motion for Standard and Poor (SP) 500 stock data. Simulation result give BNS Gamma Ornstein-Uhlenbeck model and Geometric Brownian motion a Root Mean Square Error (RMSE) are 0,13 and 0,24 respectively. These result indicate that the BNS Gamma Ornstein-Uhlenbeck model gives a more accurate than Geometric Brownian motion
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/27941
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/27941/91145
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/23925
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
MODELING EAST JAVA INDONESIA LIFE EXPECTANCY USING SEMIPARAMETRIC REGRESSION MIXED SPLINE TRUNCATED AND FOURIER SERIES
Nisa', Khaerun
Balai Penelitian dan Pengembangan Agama Makassar (BLAM)
Budiantara, I Nyoman
Institut Teknologi Sepuluh Nopember
Life Expectancy, Semiparametric Regression, Mixed Estimator, Spline Truncated, Fourier Series
Angka Harapan Hidup merupakan salah satu indikator yang digunakan untuk mengevaluasi kinerja pemerintah dalam meningkatkan kesejahteraan penduduk. Angka Harapan Hidup yang tinggi di suatu daerah mengindikasikan bahwa masyarakat di daerah tersebut telah terjamin kesehatannya dan kemiskinannya sudah diatasi dengan baik, begitu pula sebaliknya. Berdasarkan data Survei Sosial Ekonomi Nasional (SUSENAS), menunjukkan Angka Harapan Hidup di Provinsi Jawa Timur dari tahun 2009 hingga tahun 2013 mengalami peningkatan yakni 69,15 tahun dan 70,19 tahun. Meskipun secara keseluruhan Angka Harapan Hidup di Provinsi Jawa Timur mengalami peningkatan, namun masih terdapat beberapa daerah yang memiliki Angka Harapan Hidup dibawah 65 tahun. Hal ini tidak terlepas dari adanya perbedaan karakteristik yang berbeda-beda dari setiap wilayah. Maka dari itu tujuan utama dari penelitian ini yaitu melakukan pemodelan Angka Harapan Hidup di Jawa Timur menggunakan regresi semiparametrik dengan estimator campuran Spline Truncated dan Deret Fourier. Berdasarkan penelitian yang telah dilakukan, diperoleh hasil bahwa pemodelan data Angka Harapan Hidup di Jawa Timur menggunakan estimator campuran Spline Truncated dan Deret Fourier menghasilkan nilai R2 sebesar 99,62% yang berarti bahwa variabel-variabel prediktor mampu menjelaskan variabel respon Angka Harapan Hidup sebesar 99,62%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/23925
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/23925/66934
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/24510
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
IDENTIFICATION OF RAINFALL DISTRIBUTION IN WEST SUMATERA AND ASSESSMENT OF ITS PARAMETERS USING BAYES METHOD
Yanuar, Ferra
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
Sari, Putri Trisna
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
Asdi, Yudiantri
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
Bayes Method; Maximum Likelihood Estimation; Rainfall Data.
One distribution of rainfall data is a lognormal distribution with location parameters and scale parameters . This study aims to estimate the mean and variance of rainfall data in several selected cities and regencies in West Sumatra. Parameter estimation is estimated by using maximum likelihood estimation (direct method) and Bayes method. This study resulted that the Bayes method produces a better predictive value with a smaller variance value than with direct estimation. It was concluded that the estimation by the Bayes method was a better estimator method than the direct estimation.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24510
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5665
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP
Hanni, Tuan
Wuryandari, Triastuti
A lot of events occured in daily life are connected with survival time, for example a time interval that measure the failure of a product, time duration which is needed to recover from disease, the back pain recurred after treatment. Data about survival time duration of an event is called survival data. Survival data can not be observed completely that is called as sensored data. Cox proportional hazard model is employed to analyze and determine the survival rate from cencored data affected one or more explanatory variables. This model assummed that the hazard rate of group is proportional to the hazard rate of another group. In the paper, wants to the factor that affect the survival of patient with cervical cancer. From the result of data processing that affect are age and stadum with cox proportionl hazard model is hi(t) = exp(-1.848U1i – 1.584U2i – 3.255S2i - 2.108S3i ) h0(t)
Keywords : Cox Proportional Hazard, Survival Rate, Hazard Rate, Cervical Cancer
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5665
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/33025
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
THE APPLICATION OF THE SEMIPARAMETRIC GSTAR MODEL IN DETERMINING GAMMA-RAY LOG DATA ON SOIL LAYERS
Yundari, Yundari
Mathematics Department, FMIPA, Universitas Tanjungpura
Martha, Shantika
Statistics Department, FMIPA, Universitas Tanjungpura
space-time model; spatial weighted; superposition of rock-layer
This research examines the semiparametric Generalized Space-Time Autoregressive (GSTAR) spacetime modeling and determines its spatial weight. In general, the spatial weights used are uniform, binary weights, and based on the distance, the result is a fixed weight. The GSTAR model is a stochastic model that takes into account its random variables. Thus, it is necessary to study the random spatial weights. This study introduced a new method to estimate the observed value of the GSTAR model semiparametric with a uniform kernel. The data involved the Gamma Ray (GR) log data on four coal drill holes. The semiparametric GSTAR modeling aimed to predict the amount of log GR in the unobserved soil layer based on the observation data information on the layer above it and its surrounding location. The results revealed that semiparametric GSTAR modeling could predict the presence of coal seams and their thickness of drill holes. The results also highlight the validity test on the out-sample data that the error in each borehole results in a small error. In addition, the error tends to approach the actual observed value at a depth of 1 meter down.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/33025
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8489
2018-02-27T10:51:04Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
PEMODELAN VOLATILITAS UNTUK PENGHITUNGAN VALUE AT RISK (VaR) MENGGUNAKAN FEED FORWARD NEURAL NETWORK DAN ALGORITMA GENETIKA
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Suparti, Suparti
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). This model can serve as a model input in the model Feed Forward Neural Network (FFNN) with Genetic Algorithms as a training algorithm, known as GA-Neuro-GARCH. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a model GA-Neuro-GARCH on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (1,0,1) -GARCH (1,1), so that the model used FFNN consists of 2 units of neurons in the input layer, 5 units of neurons in the hidden layer neuron layer and 1 unit in the output layer. then using a genetic algorithm with crossover probability value of 0.4, was obtained that the Mean Absolute Percentage Error (MAPE) of 0,0039%.
Keywords: FFNN, Genetic Algorithm, GARCH, Volatility
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8489
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/51902
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"230615 2023 eng "
2477-0647
1979-3693
dc
SUPPORT VECTOR REGRESSION (SVR) METHOD FOR PADDY GROWTH PHASE MODELING USING SENTINEL-1 IMAGE DATA
Muradi, Hengki
Remote Sensing Research Center National Research and Innovation Agency
Saefuddin, Asep
Department of Statistics Bogor Agricultural Institute https://orcid.org/0000-0002-1694-9515
Sumertajaya, I Made
Department of Statistics Bogor Agricultural Institute https://orcid.org/0000-0002-2682-7941
Soleh, Agus Mohamad
Department of Statistics Bogor Agricultural Institute https://orcid.org/0000-0002-2732-1985
Domiri, Dede Dirgahayu
Remote Sensing Research Center National Research and Innovation Agency
Support Vector Machines;Linear Regression;Accuracy;Stability
Support Vector Machines (SVMs) have received extensive attention over the last decade because it is claimed to be able to produce models that are accurate and have good predictions in various situations. This study aims to test the SVR (Support Vector Regression) method for modeling the growth phase of paddy using sentinel-1 image data. This method was compared for its accuracy with the LR (Linear Model) method using RMSE and R2 statistics and model stability using 10 repetitions. The accuracy of the model with the two best predictors is when the NDPI and API Polarization Index are the predictors. The paddy age model from the SVR method is better than the paddy age model from the LR method, where the SVR method produces a model with an average RMSE of 11.13 and an average coefficient of determination of 88.10%. The accuracy of the SVR model with NDPI and API predictors can be improved by adding VH polarization to the model, where the average RMSE statistic decreases to 11.0 and the average coefficient of determination becomes 88.42%. In this scenario, the best model gives a minimum RMSE value of 10.35 and a coefficient of determination of 90.05%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/51902
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/51902/165878
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/51902/177548
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/42085
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
LIFE EXPECTANCY MODELING USING MODIFIED SPATIAL AUTOREGRESSIVE MODEL
Yasin, Hasbi
Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University https://orcid.org/0000-0002-4887-9646
Warsito, Budi
Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University
Hakim, Arief Rachman
Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University
Azizah, Rahmasari Nur
Data Science Institute, I-Biostat, Hasselt University Belgium
Life Expectancy; MSOM; Outlier detection; SAR
The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/42085
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/42085/129587
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10085
2018-02-27T10:09:30Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
PEMODELAN DATA INFLASI INDONESIA PADA SEKTOR TRANSPORTASI, KOMUNIKASI, DAN JASA KEUANGAN MENGGUNAKAN METODE KERNEL DAN SPLINE
Suparti, Suparti
Jurusan Statistika FSM Undip
Tarno, Tarno
Jurusan Statistika FSM Undip
In this research, we study data modeling of Indonesian inflation in the transportation, communication and financial services sector using the kernel and spline models. Determination of the optimal models based on the smallest of GCV value and determination of the best model based on the smallest out sampels of Mean Square Error (MSE) value. By modeling the yoy (year on year) inflation data in Indonesia in the transportation, communication and financial services sector In January 2007 to January 2015, shows that the kernel model using Gaussian kernel function obtained optimal model with a bandwidth 0.24 and the optimal spline model with order 5 and 4 points knots. Based on out sampels data in February to August 2015, obtained out sampels MSE value of the spline model is smaller than the kernel model. So that the spline model is better than the kernel model to analyze the inflation data of transportation, communication and financial services sector.
Keywords: Inflation, Transportation, Communication and Financial Services Sector, Kernel, Spline, GCV, MSE.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10085
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/15599
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Perbandingan Sensitivitas Harga Obligasi Berdasarkan Durasi Macaulay dan Durasi Eksponensial dengan Pengaruh Konveksitas (Studi Empiris pada Data Obligasi Korporasi Indonesia yang Terbit Tahun 2015)
Maruddani, Di Asih I
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Hoyyi, Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Macaulay duration has often been used as a measure of the bond prices sensitivity to changes in interest rates. For a small change in interest rates, the duration provides a good approximation of the actual change in price. As the change in interest rates gets larger, the duration approximation has larger errors. The convexity of bond prices change is often used as a way to improve the accuracy of the approximation. Several authors have pointed out that the natural logarithm of bond price is a better measure of percentage changes in bond prices as interest rates change. Based on this idea, this paper derives an accurate method of estimating percentage bond price changes in response to changes in interest rates, which is called exponential duration. This paper gives new estimation of bond prices using exponential duration with convexity approach. It will be shown that the new estimation bond prices is always more accurate than by Macaulay duration with convexity approach. For empirical study, it is used corporate bond data, which is published by Indonesian Bond Pricing Agency in 2015. The result support the theory that error value of Macaulay duration with convexity is more than the error value of exponential duration with convexity.
Keywords:
Bond Price, Convexity, Exponential Duration, Macaulay Duration, Modified Duration
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15599
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2471
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI
Pramesti, Getut
An Aggregation of claims are claims the sum of individual claims can be described in a distribution of collective risks that occur in a single period of insurance. Distribution is depicted in a probability density function and cumulative density function. These functions can also describe the characteristics of the distribution through the mean and variance. Writing this paper is to determine the aggregate claims model with a amount individual claims Rayleigh distributed and the number of claims Poisson distributed. Discussion of the results obtained showed that the model's claim depends on the aggregation of individual claims and the number of claims that occurred during the period of insurance.
Keywords: Aggregation, Claim, Rayleigh
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2471
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18407
2018-04-04T11:31:02Z
media_statistika:FMT
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18407
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2503
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
IMPLEMENTASI MARKOV CHAIN MONTE CARLO PADA PENDUGAAN HYPERPARAMETER REGRESI PROSES GAUSSIAN
Mukid, Moch. Abdul
Sugito, Sugito
This paper studies the implementation of Markov Chain Monte Carlo on estimating the hyperparameter of Gaussian process. Metropolish-Hasting (MH) algorithm is used to generate the random samples from the posterior distribution that can not be generated by a direct simulation method. This algorithm require only a proposal distribution for generating a candidate point. In this paper uniform distribution is choosen as the proposal distribution.
Keywords: Markov Chain Monte Carlo, Gaussian Process, Metropolis-Hasting Algorithm
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2503
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20842
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
PREDIKSI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS (MSP)
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Warsito, Budi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Hakim, Arief Rachman
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
This research covers Spatial Extreme Value method application with Max-Stable Process (MSP) approach that will be used to analysis Extreme Rainfall in Semarang city. Extreme value sample are selected by Block Maxima methods, it will be estimated into Spatial Extreme Value form by including location factors. Then it transform to Frechet distribution because it has a heavy tail pattern. Max Stable Process (MSP) is an extension of the multivariate extreme value distribution into infinite dimension of the Extreme Value Theory. After the best model of extreme rainfall data in Semarang is obtained, then calculated the prediction of extreme rainfall with a certain time period. Predictions are calculated using a return level, predictions of extreme rainfall using the return period of the next two years, at the Semarang City Climatology Station predicted to be a maximum of 100.7539 mm. At the Tanjung Mas Rain Monitoring Station it is predicted that a maximum of 100.1052 mm, Ahmad Yani Rain Monitoring Station is predicted to be a maximum of 109.9379 mm. Maximum prediction of extreme rainfall can also be calculated for future t (time) periods.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20842
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2524
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
ANALISIS EFISIENSI BANK PERKREDITAN RAKYAT DI KOTA SEMARANG DENGAN PENDEKATAN DATA ENVOLEPMENT ANALYSIS
Septianto, Hendi
Widiharih, Tatik
The research was conducted to measure rural banks (Bank Perkreditan Rakyat / BPR) efficiency level in Semarang city. The measurement was done using non parametric approach with Data Envolepment Analysis (DEA) method constant return to scale assumption (CCR model). The research was using all rural banks in Semarang (16 rural banks). The result indicated that 6 rural banks were efficient and 10 rurals banks were inefficient.
Keywords: CCR Model, Efficient, Rural Bank
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2524
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/25407
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
HAZARD PROPORTIONAL REGRESSION STUDY TO DETERMINE STROKE RISK FACTORS USING BRESLOW METHOD
Sudarno, Sudarno
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro https://scholar.google.co.id/citations?user=LY2xbq0AAAAJ&hl=id
Setiani, Eri
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Breslow method; Cox proportional hazard regression; Schoenfeld residual; Stroke
Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular be death. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. The objectives of this research are applied Cox proportional hazard regression on ties event using Breslow methodand determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. The response variable is length of stay at hospital, and the predictors are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and body mass index. The factors cause stroke disease by significant are type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, and blood sugar level. By the survivorship function that the patients have been looked after at hospital greater than 20 days, they have probability of healthy be little even go to death. A person in order to be healthy must notice and prevent some factors cause disease.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/25407
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4520
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
ANALISIS CLUSTER PADA KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PRODUKSI PALAWIJA
Safitri, Diah
Widiharih, Tatik
Wilandari, Yuciana
Saputra, Arsyil Hendra
Production of palawija, namely maize, cassava, sweet potato, peanut, soybean, and green bean is an important food crop in Central Java. In this article, districts/cities in Central Java are grouped into three groups based on the production of palawija so as to know which group have high potential the production of maize, cassava, sweet potato, peanut, soybean or green bean by using k-means cluster analysis. Cluster 1 consists of District Cilacap, Wonosobo, Magelang, Karanganyar, Semarang, Temanggung, Kendal, and Batang that have a high potential in maize production. Cluster 2 consists of District Banyumas, Purbalingga, Banjarnegara, Kebumen, Purworejo, Boyolali, Klaten, Sukoharjo, Sragen, Blora, Rembang, Pati, Kudus, Jepara, Demak, Pekalongan, Pemalang, Tegal, Brebes, Magelang City, Surakarta City, Salatiga City, Semarang City, Pekalongan City, and Tegal City that have a high potential in peanut production. Cluster 3 consist of District Wonogiri and Grobogan that have a high potential in soybean production, green bean production, cassava production, and sweet potato production
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4520
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/4834
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
PENENTUAN MODEL ANTRIAN BUS ANTAR KOTA DI TERMINAL MANGKANG
Ispriyanti, Dwi
Sugito, Sugito
In daily activities, we often face in a situation of queueing. The Queue is dull. Most people have experienced in a queue situation or a waiting situation. It is a part of the state that occurs in a series of operations that are random in a service facility. The Queue can be found easily in a human life, for example bus queue in Terminal Mangkang. It means that a bus wait to be dispatched and from the bus that will go to the service station. Therefore make an arrival and departure of buses not on schedule which resulted in the accumulation of customers in the terminal. To analyze the extent of the effectiveness of terminal Mangkang particularly inter-city terminal Queue theory it is used in the service system in the terminal.
Keywords: Queue, Terminal Mangkang
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4834
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/33550
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
UTILIZATION OF STUDENT’S T DISTRIBUTION TO HANDLE OUTLIERS IN TECHNICAL EFFICIENCY MEASUREMENT
Zulkarnain, Rizky
BPS Statistics Indonesia https://orcid.org/0000-0003-4602-2515
Djuraidah, Anik
BPS Statistics Indonesia
Sumertajaya, I Made
Department of Statistics, IPB University
Indahwati, Indahwati
Department of Statistics, IPB University
Cobb-Douglas; maximum simulated likelihood; robust; productivity; SFA; Translog
Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Student’s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/33550
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8289
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
GRAFIK PENGENDALI RAGAM SAMPEL UNTUK MONITORING VARIABILITAS PROSES PRODUKSI
Sudarno, Sudarno
The control chart is a graphical display or a quality characteristic that has been measured or computed from a sample versus the sample number or time. The variance chart is used to monitoring variability of production process. It is an altenative way to check variability process rather than R chart or s chart. The problems will be done are find the parameters of variance chart, predict process capability, verify defect per million opportunities (DPMO) of process result and simulation kinds of shift sigma values. This result could be used as information to production process at the future time. The result of discussion that upper conrol limit = 0.0014, center line = 0.00073, lower control limit = 0.00028, process capability = 1.003 and DPMO = 2,620 part per million. These parameters used for information in the next production process.
Keywords: Variance Chart, Process Capability, Defect per million opportunities, Shift.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
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https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8289
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/37096
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL
Hesikumalasari, Hesikumalasari
Universitas Islam Negeri Mataram
Budiantara, I Nyoman
Institut Teknologi Sepuluh Nopember
Ratnasari, Vita
Institut Teknologi Sepuluh Nopember
Nisa', Khaerun
Balai Penelitian dan Pengembangan Agama Makassar
Semiparametric regression;mixed estimator;linear truncated spline;kernel;multivariable
The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
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https://ejournal.undip.ac.id/index.php/media_statistika/article/view/37096
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/37096/150439
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9203
2018-02-27T10:12:08Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
MODEL EKSPONENSIAL GANDA PADA PROSES STOKASTIK (STUDI KASUS DI STASIUN PURWOSARI)
Sugito, Sugito
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Wilandari, Yuciana
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
In general, mathematical modeling is divided into two, namely the model of deterministic and stochastic models. On stochastic modeling involves several processes among them are the Poisson process, the process of Bernoulli, Gaussian processes, the process of renewal and other processes. Specifically for the Poisson process often found in modeling queuing theory. At Poisson process there are four kinds of sub model that can be formed that is Double Poisson models, Exponential Poisson models, Poisson Exponential model, and Double Exponential models. In this paper will discuss the Double Exponential model in stochastic processes , specifically for the Poisson process. Analysis was performed on the data arrival time and service time. The model is a model (M / M / c) : ( GD / ~, ~) which is a double exponential model in stochastic processes.
Keywords: Double Exponential, Poisson Process, Stochastic Process
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
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https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9203
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/56191
2024-02-26T03:51:54Z
media_statistika:ART
nmb a2200000Iu 4500
"240226 2024 eng "
2477-0647
1979-3693
dc
MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION
Aswi, Aswi
Statistics Study Program, Universitas Negeri Makassar http://orcid.org/0000-0002-0639-2936
Tiro, Muhammad Arif
Statistics Department, Universitas Negeri Makassar
Sudarmin, Sudarmin
Statistics Department, Universitas Negeri Makassar
Sukarna, Sukarna
Mathematics Department, Universitas Negeri Makassar
Awi, Awi
Mathematics Department, Universitas Negeri Makassar
Nurwan, Nurwan
Statistics Department, Universitas Negeri Makassar
Cramb, Susanna
Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology, Australia
Bayesian spatial; CAR Leroux; Relative Risk; R Shiny; Disease Mapping.
Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-12-22 11:43:06
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https://ejournal.undip.ac.id/index.php/media_statistika/article/view/56191
MEDIA STATISTIKA; Vol 16, No 2 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/45557
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL
Sukarna, Sukarna
Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Syahrul, Nurul Fadilah
Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Sanusi, Wahidah
Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Aswi, Aswi
Statistics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Abdy, Muhammad
Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
Irwan, Irwan
Mathematics Department, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Indonesia
estimating; forecasting; GSTARIMA; covid-19
A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45557
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13132
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
MODEL PENILAIAN KREDIT MENGGUNAKAN ANALISIS DISKRIMINAN DENGAN VARIABEL BEBAS CAMPURAN BINER DAN KONTINU
Mukid, Moch. Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Widiharih, Tatik
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Credit scoring models is an important tools in the credit granting process. These models measure the credit risk of a prospective client. This study aims to applied a discriminant model with mixed predictor variables (binary and continuous) for credit assesment. Implementation of the model use debitur characteristics data from a bank in Lampung Province which the used binary variables involve sex and marital status. Whereas, the continuous variables that was considered appropriate in the model are age, net income, and length of work. By using the data training, it was known that the misclassification of the model is 0.1970 and the misclassification of the testing data reach to 0.3753.
Keywords: discriminant analysis, mixed variables, credit scoring
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13132
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/250
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KESEMBUHAN PASIEN PENYAKIT FLU BURUNG
Wilandari, Yuciana
Safitri, Diah
Avian influenza is contagion which caused by influenza virus type H5N1 often cause death. Avian influenza anticipated to be influenced by gender, age, epidemiology and case, to know the factors have a significant effect used by independent test. is later on made model of regresi binary logistics. Then obtained by factor having an effect is case and epidemiology, that is made regression logistics model. Someone which including case of suspect to be able to have probability recover bigger than someone which including confirmation case, someone which contact with dead an avian to be able to have probability recover smaller than someone which no contact.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
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https://ejournal.undip.ac.id/index.php/media_statistika/article/view/250
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18394
2018-04-07T09:20:32Z
media_statistika:FMT
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18394
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2498
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
METODE TAGUCHI UNTUK OPTIMALISASI PRODUK PADA RANCANGAN FAKTORIAL
Wuryandari, Triastuti
Widiharih, Tatik
Anggraini, Sayekti Dewi
Taguchi methods represent the effort quality improvement which known as off-line quality control method because the method design quality into every appropriate process and product. Taguchi methods is represent quality repair with attempt “new” methods, its meaning do dissimilar approach giving same belief storey by SPC (Statistical Proces Control), very effective in quality improvement as well as lessening expense of same. Fractional factorial design represent base from Taguchi method by fraction from factorial design. Fractional factorial with 4 factors and defining relations p = 2 is or 81 run become or 9 blocks with each blocks there are 9 run just eligible one block. The block name that is Orthogonal Array which lessen time and attemp fare. Orthogonal Array used to device of factorial attemp 3 level by 4 factors that is Orthogonal Array L9. Optimalitation product of factorial design can be determinate with tables of anova, table of response and tables of Signal to Noise Ratio.
Keywords: Taguchi Methods, Signal to Noise Ratio, Orthogonal Array
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2498
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18701
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
Ningsih, Wiwik Andriyani Lestari
Arcana, I Made
Two aspects of efficiency that should be considered in applying sampling design of a survey are statistical efficiency and cost efficiency. Efficiency in statistical aspect improves precision of estimators obtained by the survey data, whereas efficiency in cost aspect provides an economic survey. The purpose of this researchis to evaluate the both efficiencies in all possible census blocks (CBs) sample setand to identify the best CBs sample set in the 2015 National Socio-Economic Survey (Susenas). Therefore, a computer program for calculating statistical, and cost efficiency aspects was developed in this research to determine the best sampel set of CBs among all possible sampel set of CBs based on sampling design of the 2015 Susenas implemented in Natuna District, Kepulauan Riau Province. The best possible sample set of CBs is determinedby considering statistical efficiency aspect, cost efficiency aspect, as well as combination of those two aspects. The result showed that the best sample set of CBs on statistical efficiency aspect provided the CBs sample set having minimum value of RSE index; evaluation on cost efficiency aspect provided the best CBs sample set having minimum value of total cost esimated using the total score of accessibility index; and evaluation on both efficiency aspects provided the best CBs sample set having minimum value of RSE index and minimum value of total score of accessibility index.
Keywords: sampling design, all possible samples, statistical efficiency, cost efficiency
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18701
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
ind
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2514
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
ANALISIS KORESPONDENSI UNTUK PEMETAAN PERSEPSI
Rusgiyono, Agus
Correspondence analysis used to investigate the relationship between two or more qualitative variables. This technique could shrink the dimensions of variables and describe the profile vector of rows and columns of a matrix vector data from the contingency table. Target correspondence analysis is to show the relationship variables rows and columns as well as visualization variables in R2-dimensional space, using the Chi square of the distance definition in sub-Euclidean space.
Keywords: Profile of Row and Column Vectors, Chi Square Distance, Euclidean Subset
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2514
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/25892
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
MODELING CENTRAL JAVA INFLATION AND GRDP RATE USING SPLINE TRUNCATED BIRESPON REGRESSION AND BIRESPON LINEAR MODEL
Suparti, Suparti
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Prahutama, Alan
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Rusgiyono, Agus
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Sudargo, Sudargo
PGRI Semarang University
Inflation; GRDP; Spline Biresponse
Inflation and Gross Regional Domestic Income (GRDP) are two macroeconomic variables of a region that are correlated with each other. GRDP prices constant (real) can be used as an indicator of economic growth in a region from year to year. Inflation is calculated from the CPI rate and economic growth is calculated from the GRDP rate. Inflation and economic growth in an area are influenced by several factors including bank interest rates. Analysis of data consisting of 2 correlated responses can be performed with birespon regression analysis. In this research, modeling of inflation data and the rate of GRDP through birespon data modeling uses spline truncated nonparametric method and birespon linear parametric method. The purpose of this study is to model inflation data and the Central Java GRDP rate using spline truncated birespon regression. The results are compared with the birespon linear regression model. By using quarterly data from the first quarter of 2007 - the second quarter of 2019, the spline truncated model is better than the linear model, because the spline truncated model has a smaller MSE and R2 is greater than the linear model. Both models have the same performance which is quite good.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/25892
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4524
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI BANYAKNYA KLAIM ASURANSI KENDARAAAN BERMOTOR MENGGUNAKAN MODEL REGRESI ZERO-INFLATED POISSON (Studi Kasus di PT. Asuransi Sinar Mas Cabang Semarang Tahun 2010)
Taufan, Muhammad
Suparti, Suparti
Rusgiyono, Agus
Poisson regression is one of model that is often used to model the relationship between response variables in the form of discrete data with a set of predictor variables in the form of continuous, discrete, category, or mixture data. In Poisson regression assumes that the mean of the response variable equal to the variance (equidispersion). But in reality, sometimes found a condition called overdispersion, that the variance value is greater than the mean. One of the cause of overdispersion is excess zero in the response variable. One of model that can be used to overcome this overdispersion problem is Zero-Inflated Poisson (ZIP) regression model. This model is applied on a case study of motor vehicle insurance in the branch of PT. Asuransi Sinar Mas in Semarang in 2010 to determine the effect of age of car and types of coverage to number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang. In this case, the occurrence of zeros due to many policyholders did not file a claim to the branch of PT. Asuransi Sinar Mas in Semarang. From the analytical result obtained the conclution that the age of car and types of coverage affect number of claims filed by the policyholder to the branch of PT. Asuransi Sinar Mas in Semarang in 2010.
Keywords: Poisson Regression, Overdispersion, Zero-Inflated Poisson (ZIP) Regression
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4524
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22634
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
APLIKASI ERROR CORRECTION MECHANISM DALAM ANALISIS DAMPAK PERTUMBUHAN EKONOMI, KONSUMSI ENERGI DAN PERDAGANGAN INTERNASIONAL TERHADAP EMISI CO2 DI INDONESIA
Kartiasih, Fitri
Politeknik Statistika STIS
Setiawan, Adi
BPS Prov. DKI Jakarta
CO2 Emissions; ECM; Energy Consumption; Environment Kuznets Curve; Trade
Economic development is an effort to improve people's lives. However, economic development has negative externalities. Emissions generated from economic activities can pollute the environment. This study purpose to determine the relationship between economic growth and CO2 emissions based on the Environment Kuznets Curve (EKC) hypothesis and analyze the influence of energy use, economic growth and international trade on CO2 emissions in Indonesia in the period 1977-2014 using Error Correction Mechanism (ECM) analysis. The results showed that the EKC hypothesis does not apply in Indonesia, meaning that economic development carried out during the research period still pursues increased income without regard to environmental quality so that increased per capita income is accompanied by increase in CO2 emissions. Based on econometric analysis of ECM, it shows that the variables of energy use, economic growth and international trade have a statistically significant effect on CO2 emissions in Indonesia in the long run. In the short term, economic growth, and error correction terms have a statistically significant effect while the variables of energy consumption and international trade do not have a statistical effect on CO2 emissions in Indonesia.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22634
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/22634/91244
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/25146
2021-06-30T10:13:32Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
SMALL AREA ESTIMATION METHOD WITH EMPIRICAL BAYES BASED ON BETA BINOMIAL MODEL IN GENERATED DATA
Yanuar, Ferra
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
Fajriyah, Rahmatika
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
Devianto, Dodi
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Andalas
SAE; Empirical Bayes Method; Beta-Binomial Model.
Small Area Estimation (SAE) is one of the statistical methods that used to estimate parameters of model from small subpopulations. Because of that, additional information is needed to predict these parameters that will result a more accurate predictive value. The Bayes empirical method is a method in SAE that uses the Bayes method in estimating parameter value. This method can be used to process binary data by using Beta-Binomial models both theoretically and simulation study. This study aims to implement the simulation study to describe Beta Binomial model using empirical Bayes in SAE approach. To illustrate the method, we consider three conditions of the models, those are classical estimator, empirical Bayes with indicator variables and empirical Bayes without indicator variable. This study proved that empirical Bayes with indicator variable gave a better parameter estimation result with the smallest mean square errors compared to the others model.
Keywords: SAE, Empirical Bayes Method, Beta-Binomial Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/25146
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7640
2016-03-15T17:19:31Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
ANALISIS VARIABEL KANONIK BIPLOT UNTUK BANK UMUM DI JAWA TENGAH
Yasin, Hasbi
Rusgiyono, Agus
Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in Central Java with the Canonical Variate Analysis (CVA) Biplot. This analysis can be applied to find out information about the relative position, the similarity between the object characteristics and diversity of variables in the three groups of commercial banks in Central Java, namely state-owned banks, private banks and private banks Non Foreign Exchange, based on the health aspects of the bank. The results obtained are the banks in each group had different characteristics shown in the relative position of the already well-separated in the resulting biplot. Variables that tend to influence the grouping of commercial banks are Capital Adequacy Ratio (CAR). The total assets is variable with the highest level of prediction accuracy on each bank.
Keywords: Health Aspects of the Bank, Commercial Banks, Canonical Variate Analysis (CVA) Biplot.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7640
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/38947
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION
Miftahuddin, Miftahuddin
Department of Statistics, Faculty of Mathematics and Sciences, Universitas Syiah Kuala https://orcid.org/0000-0002-6414-4498
Putri, Retno Wahyuni
Department of Statistics, Faculty of Mathematics and Sciences, Universitas Syiah Kuala
Setiawan, Ichsan
Department of Marine, Faculty of Mathematics and Sciences, Universitas Syiah Kuala
Oktari, Rina Suryani
Department of Family Medicine, Faculty of Mathematics and Sciences, Universitas Syiah Kuala
Sea surface temperature; latent variables; PLS-SEM; weather; climate; El-Nino and La-Nina.
Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/38947
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9198
2018-02-27T10:49:48Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
ANALISIS DATA INFLASI INDONESIA MENGGUNAKAN MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DENGAN PENAMBAHAN OUTLIER
Suparti, Suparti
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Sa'adah, Alfi Faridatus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The inflation data is one of the financial time series data which often has high volatility. It is caused by the presence of outliers in the data. Therefore, it is necessary to analyze forecasting that can make all the assumptions are fulled without having to ignore the presence of outliers. The aim of this study is analyzing the inflation data in Indonesia using ARIMA model with the outlier detection. By modeling annual inflation data in December 2006 to December 2013 there are two types of outlier that are additive outlier (AO) and level shift (LS) outlier. The results show that The ARIMA model with the addition of outlier are better than the ARIMA model without outlier. The ARIMA ([1.12], 1.0) model with the addition of 19 outliers meet to the all assumptions that are the significance parameters, normality, homoscedasticity, and independence of residuals as well as the smallest MSE value.
Keywords: Inflation, ARIMA, Outlier, MSE
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9198
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/54430
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231207 2023 eng "
2477-0647
1979-3693
dc
BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH
Sunandi, Etis
Department of Statistics, IPB University, Indonesia
Statistics Program, Department of Mathematics, University of Bengkulu, Bengkulu, Indonesia
Notodiputro, Khairil Anwar
Department of Statistics, IPB University, Indonesia
Indahwati, Indahwati
Department of Statistics, IPB University, Indonesia
Soleh, Agus Mohamad
Department of Statistics, IPB University, Indonesia
Area level; Binary Response; Illiteracy rate; MSEP; Simulation; Small sample
Small Area Estimation is a statistical method used to estimate parameters in sub-populations with small or even no sample sizes. This research aims to evaluate the Beta-Binomial model's performance for estimating small areas at the area level. The estimation method used is Hierarchical Likelihood (HL). The data used are simulation data and empirical data. Simulation studies were used to investigate the proposed model. The estimator's Mean Squared Error of Prediction (MSEP) and Absolute Bias (AB) estimator values determine the best estimation criteria. An empirical study using data on the illiteracy rate at the sub-district level in Bengkulu Province. The results of the simulation study show that, in general, the parameter estimators are nearly unbiased. Proportion prediction has the same tendency as parameters. Finally, the HL estimator has a small MSEP estimator. The results of an empirical study show that the average illiteracy rate in Bengkulu province is quite diverse. Kepahiang District has the highest average illiteracy rate in Bengkulu Province in 2021.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/54430
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/43250
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
COLLABORATIVE FILTERING APPROACH: SKINCARE PRODUCT RECOMMENDATION USING SINGULAR VALUE DECOMPOSITION (SVD)
Nissa, Farhatun
Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia
Primandari, Arum Handini
Statistics Study Program, Faculty of Mathematics and Natural Science, Universitas Islam Indonesia https://orcid.org/0000-0002-1977-8782
Thalib, Achmad Kurniansyah
Purwadika Digital Technology School
Collaborative filtering; Singular Value Decomposition; Skincare recommendation;
The recommendation system provides recommendations for something, be it goods, songs, or movies. The term system is not limited to a service system but concerns a model that can provide recommendations. With recent technological advances, many companies provide various skincare products because current generations are increasingly aware of self-care. With various choices, someone may experience confusion in determining the product they want to buy. Therefore, we need a system that can provide product recommendations run on any platform we use. The most common method for recommendation systems often comes with Collaborating Filtering (CF) where it relies on the past user and item dataset. The singular value decomposition (SVD) method uses a matrix factorization technique that predict the user's rating based on historical ratings. The measurement of the model's accuracy is the RMSE average of 1.01276, indicating that this value results from the best parameters. The results focus on showing skincare product recommendations to users sorted based on rating predictions.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/43250
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11723
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
ANALISIS KECELAKAAN LALU LINTAS DI KOTA SEMARANG MENGGUNAKAN MODEL LOG LINIER
Wilandari, Yuciana
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Sugito, Sugito
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Silvia, Candra
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Traffic accident is an event in the unanticipated and unintended involve vehicles with or without other road users, resulting in losses and/or loss of property. According Polrestabes Semarang number of traffic accidents decreased in 2014 compared to 2013, but the figure is still considered high. Therefore we need an analysis of traffic accident cases, in this case using a log linear models. Log linear models used to analyze the relationship between the response variables that are categories that make up the contingency table and determine which variables are likely to cause depedensi. In this study, the variable used is the severity of the victim, the type of accident, the role of the victim, the victim vehicle type, time of the accident and the age of the victim. The results indicate that the variables that affect the model is the severity of the victim, the type of accident, the role of the victim, the type of vehicle the victim, time of the accident, the age of the victim, the role of the victim * type of vehicle the victim, the type of accident * the role of the victim, the type of vehicle the victim * age of the victim, the type of accident * type of vehicle the victim, the severity of the victim * type of accident, type of accident * age of the victim. So that raises the most variable attachment is a type of accident.
Keywords : Traffic Accident, Log Linear Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11723
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18272
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
ANALISIS DISKRIMINAN BERGANDA DENGAN PEUBAH BEBAS CAMPURAN KATEGORIK DAN KONTINU PADA KLASIFIKASI INDEKS PRESTASI KUMULATIF MAHASISWA
Walidaini, Nur
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Mukid, Moch. Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Prahutama, Alan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Rusgiyono, Agus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Multiple discriminant analysis is one of the discriminant analysis techniques where the dependent variable are grouped into more than two groups. This paper discussed how to categorize Grade Point Average (GPA) of undergraduate student of Faculty of Sciences and Mathematics Diponegoro University based on categorical and continuous independent variable including gender, internet usage, time per week for learning, average score in national examination, amount of pocket money per month and the way to enter to Diponegoro University. The GPA grouping refers to the Academic Regulations of Diponegoro University i.e. satisfactory GPA (2,00 to 2,75), very satisfactory (2,76 to 3,50) and with honors (cum laude) (3,51 to 4,00). By using the multiple discriminant analysis with mixture variables, the accuration of classification based on training and testing data reach to 71,875% and 41,667% respectively.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18272
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2481
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
KEEFEKTIFAN PRAUJIAN NASIONAL MATEMATIKA TAHUN AKADEMIK 2004/2005 (Studi Kasus di SMK Negeri dan Swasta di Jakarta Selatan 06)
Hoyyi, Abdul
National pre-exam is one way of the evaluation to the student’s ability. Through national pre-exam, it would get information how far the student’s preparation to have national exam. National pre-exam is expected to improve student’s score on national exam. In addition, national pre-exam is expected can be used to evaluate student’s preparation and it can predict national examination score. The improving of student’s achievement depends on the way the analysis of change of national examination achievement distribution and description statistics analysis national examination score. The statistics of McNemar’s test is used to know student’s preparation, because the sample is dependent. Correlation and simple linier regression analysis used for analysis prediction. The increase of national examination score not always the effect of pre-national examination. The pre-national examination can’t be used to estimate student’s preparation. The probability student that pass the national exam is higher than pre-national exam. It is caused by pre-national exam is more difficult than national exam through the same passing limit. The score of national exam prediction is obtained confidence limit wide enough. Therefore, the variant national of examination achievements is quite large.
Key words: National Pre-exam, National Exam, Description Analysis, McNemar’s Test; Prediction
http://ejournal.undip.ac.id/index.php/media_statistika/article/view/2481
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2481
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/16458
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
PENERAPAN REGRESI DATA PANEL PADA ANALISIS PENGARUH INFRASTRUKTUR TERHADAP PRODUKTIFITAS EKONOMI PROVINSI-PROVINSI DI LUAR PULAU JAWA TAHUN 2010-2014
Sitorus, Yosephine Magdalena
Sekolah Tinggi Ilmu Statistik
Yuliana, Lia
Sekolah Tinggi Ilmu Statistik
There is inequality between the economic growth of provinces in Java and outside of Java. The total area of Java is only 6,77% from total area of Indonesia but the Growth Domestic Product (GDP) based on constant price in 2014, Java contributed 57,8% of the GDP total Indonesia. One cause that made this disparity is the development of infrastructure in outside Java is still weak. The development of infrastructure is a basic element for increasing total output production that later will increase the economic growth. However, there are so many problems that occur in developing the infrastructure in outside of Java. This study aimed to analyze the condition of infrastructure provinces outside Java in 2010-2014. The data used is the secondary data for 27 provinces outside of Java 2010-2014 from BPS. The analytical method used is panel data regression with fixed effect model and Seemingly Unrelated Regression (SUR) Model. Based on the results, the infrastructure that affects economic productivity significantly and positively is road infrastructure, health, and budget. Infrastructure that affects economic productivity significantly and negatively is the educational infrastructure. Water and electricity infrastructure did not significantly affect economic productivity.
Keywords: Infrastructure, Economic productivity, Panel Data Regression, Fixed Effect Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/16458
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16458/40014
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2509
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
DISTRIBUSI INVERS GAMMA PADA INFERENSI BAYESIAN
Sugito, Sugito
Ispriyanti, Dwi
One of the methods which can be used in statistical inferences is Bayesian inference. It is combine sample distribution and prior distribution, that can be resulted posterior distribution. In this article, sample distribution use univariate normal distribution. If prior distribution for variance with known mean is gamma inverse distribution, then posterior distribution is formed gamma inverse distribution. If Prior distribution use non-informative prior, then have the posterior distribution, by the marginal distribution of mean and varian. Also posterior distribution formed by gamma inverse distribution.
Keywords: Gamma Inverse Distribution, Posterior Distribution, Non-Informatif Prior
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2509
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/21763
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
KLASIFIKASI KEMISKINAN DI KOTA SEMARANG MENGGUNAKAN ALGORITMA CHISQUARE AUTOMATIC INTERACTION DETECTION (CHAID) DAN CLASSIFICATION AND REGRESSION TREE (CART)
Ispriyanti, Dwi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro https://scholar.google.co.id/citations?user=-JdfACoAAAAJ&hl=id
Prahutama, Alan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Mustafid, Mustafid
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Tarno, Tarno
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Decreasing poverty level is the first goal of Sustainable Development Goals (SDGs). Poverty in Central Java from 2002 to 2017 has decreased, as well as the city of Semarang. Therefore, it is necessary to examine the factors that determine the decline in poverty classification in the city of Semarang. The classification analysis in statistics uses one classification tree. Several methods using classification trees include CART, CHAID, C45 and ID3 algorithms. In this study the methods used were CART and CHAID Algorithms. CART and CHAID algorithms are binary classification trees. The CART separation rules use split goodness op, while CHAID uses CHI-Square. In the analysis, the value of using CART was 95.2% while CHAID was 95.2%. While the factors that influence poverty classification using CHAID include the acceptance of poor rice, the main building materials of the house walls, and the main fuel for cooking. Whereas with the CART Algorithm the variables that influence are the main fuels for cooking, poor rice receipts, the number of household members, final disposal sites, sources of drinking water, the household head's business field, roofing materials, and building walls.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/21763
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2628
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
PENELUSURAN KARAKTERISTIK PERILAKU KONSUMEN DENGAN METODE AUTOMATIC INTERACTION DETECTION (AID)
Rusgiyono, Agus
AID methods used to see relation between respons variable with a number of variable of predictor and also see related pattern between predictors. In AID procedures data disjointed in two group determined by variable of predictor most explaining of difference values of respons variables, and this recuring process so that yielded to with refer to splits in data. Every split yield new data sub which its variable values is mutually and exclusive exhaustive. Its end results with refer toing crotch is so-called tree of AID
Keywords : split, tree of AID
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2628
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22904
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE
Susanto, Irwan
Program Studi Statistika, FMIPA, Universitas Sebelas Maret http://orcid.org/0000-0001-5717-9601
Handajani, Sri Sulistijowati
Program Studi Statistika, FMIPA, Universitas Sebelas Maret
Finite Mixture; Income Distribution; EM Algorithm; AIC; BIC
In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., Gamma, Lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-Lognormal mixture model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22904
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/22904/91178
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/29179
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA
Supandi, Epha Diana
Mathematics Study Program, State Islamic University Sunan Kalijaga
Generalized Structured Component Analysis; Remuneration; Structural Equation Modeling
Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/29179
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/36769
2022-07-27T00:34:48Z
media_statistika:FMT
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
Front Matter Vol. 12 No. 1 2019
Statistika, Media
Cover dan Daftar Isi Vol. 12 No. 1 Juni 2019
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36769
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
eng
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5666
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
ESTIMASI PARAMETER MODEL MIXTURE AUTOREGRESSIVE (MAR) MENGGUNAKAN ALGORITMA EKSPEKTASI MAKSIMISASI (EM)
Asrini, Mika
Sulandari, Winita
Wiyono, Santoso Budi
Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection.
Keywords : Mixture Autoregressive, EM Algorithm, BIC, Maize Production
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5666
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/34908
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
PREDICTION OF FOREST FIRE USING NEURAL NETWORKS WITH BACKPROPAGATION LEARNING AND EXREME LEARNING MACHINE APPROACH USING METEOROLOGICAL AND WEATHER INDEX VARIABLES
Rosadi, Dedi
Department of Mathematics, Gadjah Mada University
Arisanty, Deasy
Department of Geography Education, Lambung Mangkurat University
Agustina, Dina
Department of Mathematics, Padang State University
Forest fire prediction, neural networks, backpropagation, extreme learning machine
Forest fire is one of important catastrophic events and have great impact on environment, infrastructure and human life. In this study, we discuss the method for prediction of the size of the forest fire using the hybrid approach between Fuzzy-C-Means clustering (FCM) and Neural Networks (NN) classification with backpropagation learning and extreme learning machine approach. For comparison purpose, we consider a similar hybrid approach, i.e., FCM with the classical Support Vector Machine (SVM) classification approach. In the empirical study, we apply the considered methods using several meteorological and Forest Weather Index (FWI) variables. We found that the best approach will be obtained using hybrid FCM-SVM for data training, where the best performance obtains for hybrid FCM-NN-backpropagation for data testing.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/34908
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8490
2018-02-27T10:54:18Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA
Janah, Sufia Nur
Jurusan Matematika, FMIPA, Universitas Sebelas Maret
Sulandari, Winita
Jurusan Matematika, FMIPA, Universitas Sebelas Maret
Wiyono, Santoso Budi
Jurusan Matematika, FMIPA, Universitas Sebelas Maret
Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model. The hybrid model would rather be used in the short-term forecasting, no more than three periods.
Keywords: ARIMA, Backpropagation, Hybrid, Grain Price
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8490
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/53859
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231107 2023 eng "
2477-0647
1979-3693
dc
KAPLAN-MEIER AND NELSON-AALEN ESTIMATORS FOR CREDIT SCORING
Widiharih, Tatik
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro http://stat.undip.ac.id/?page_id=895
Rusgiyono, Agus
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Sudarno, Sudarno
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Saputra, Bagus Arya
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Kaplan-Meier; Nelson-Aalen; Survival Function; Cumulative Hazard Function.
Financial institutions use credit scoring analysis to predict the probability that a customer will default. In this paper, we determine the probability of default using nonparametric survival analysis that are Kaplan-Meier and Nelson-Aalen. The analysis is based on survival function curves, cumulative hazard function curves, mean survival time, and standard error of estimators. Based on the curves of survival function for both Kaplan Meier and Nelson Aalen estimators relatively the same. Based on the curves of cumulative hazard function, mean survival time, and standard error the Nelson-Aalen estimators are slightly higher than Kaplan-Meier.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/53859
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/38750
2022-07-28T02:52:59Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS
Darmawan, Gumgum
Department of Mathematics, Universitas Gadjah Mada
Department of Statistics, Universitas Padjajaran
Rosadi, Dedi
Department of Mathematics, Universitas Gadjah Mada
Ruchjana, Budi Nurani
Department of Mathematics, Universitas Padjajaran
Pontoh, Resa Septiani
Department of Statistics, Universitas Padjajaran
Asrirawan, Asrirawan
Universitas Sulawesi Barat
Setialaksana, Wirawan
Universitas Negeri Makassar
Covid-19; Singular Spectrum Analysis; FFNN; GARMA; FTS
In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/38750
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10086
2018-02-27T10:09:13Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
BAGGING CLASSIFICATION TREES UNTUK PREDIKSI RISIKO PREEKLAMPSIA (Studi Kasus : Ibu Hamil Kategori Penerima Jampersal di RSUD Dr. Moewardi Surakarta)
Mukid, Moch. Abdul
Jurusan Statistika FSM Undip
Wuryandari, Triastuti
Jurusan Statistika FSM Undip
Ratnaningrum, Desy
Jurusan Statistika FSM Undip
Sri Rahayu, Restu
Jurusan Statistika FSM Undip
Preeclampsia is a spesific pregnancy disease in which hypertency and proteinuria occurs after 20 weeks of pregnancy. Classification Trees is a statistical method that can be used to identify potency of expectant women suffering from preeclampsia. This research aim to predict the risk of preeclampsia based on some individual variables. They are parity, work status, history of hypertension of preeclampsia, body mass index, education and income. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification Trees method. By the method, classification accuracy reach to 86%.
Keywords : Pre-eclampsia, Bagging CART, Classification Accuracy
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10086
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/15600
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Penerapan Regresi Logistik Ordinal Proportional Odds Model pada Analisis Faktor-Faktor yang Mempengaruhi Kelengkapan Imunisasi Dasar Anak Balita di Provinsi Aceh Tahun 2015
Budyanra, Budyanra
Sekolah Tinggi Ilmu Statistik
Azzahra, Ghaida Nasria
Badan Pusat Statistik
Province of Aceh has basic immunization coverage toddler lowest in Indonesia in 2015. even though, this province has Posyandu and Puskesmas ratio per population of the highest in the western region of Indonesia. This data their concerns regarding immunization coverage has not been handled well in Aceh Province. This papers aims to identify variables that affect the status of complete basic immunization of children aged 12-59 months in Aceh by using ordinal logistic regression analysis. Ordinal logistic regression model used is proportional odds models. Data are obtained from Susenas 2015 that was held in March 2015 by BPS-Statistic of Indonesia. Based on the results of processing data, known only 37.7% of children aged 12-59 months in the province of Aceh in 2015 which gets fully immunized, the remaining 50.6% receive primary immunization but is not complete, even about 11.7% have not received basic immunization at all. From the proportional odds model results showed that the number of children born to mothers (odds ratio = 0.88), maternal age at delivery (odds ratio = 1.03), the level of maternal education (odds ratio = 1.22), and the educational level of the household (odds ratio = 1,2) have a significant impact on the status of complete basic immunization of children. Future studies are expected to include the element of timeliness and add other variables and also with other models in ordinal logistic regression.
Keywords:
Immunization, Ordinal Logistic Regression, Proportional Odds, Susenas
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15600
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2472
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
DISTRIBUSI POISSON DAN DISTRIBUSI EKSPONENSIAL DALAM PROSES STOKASTIK
Sugito, Sugito
Mukid, Moch Abdul
In the queueing system, the processes usually come from a Poisson process. In this system should be obtained an arrival distribution and a service distribution. This paper studies about the form of the number of arrival distribution, the number of service distribution, the interarrival distribution and the service time distribution. Futhermore it talks about the relation of them to a Poisson distribution and an exponential distribution.
Keywords: Poisson Process, Poisson Distribution, Eksponential Distribution
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2472
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18408
2020-09-30T17:36:00Z
media_statistika:FMT
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18408
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2504
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
PENGKONSTRUKSIAN KURVA YIELD DENGAN METODE NELSON SIEGEL SVENSSON (Studi Kasus Data Obligasi Pemerintah)
Setyawati, Winda
Hoyi, Abdul
Bond is one of fixed-income investment instruments because of their income granted a return for investor based on the interest rates predetermined. The level of cash that returns to the investors and factor which must be considered by investor before invest bond is called yield. The term stucture of interest rates gives the relationship between the yield on an investment and the time to maturity of the investment. The graphic depiction of the relationship between the yield on bonds in the different maturities is known as the yield curve. The yield curve contruction of the government bond with bond ID is FR (Fixed Rate) by Nelson Siegel Svensson models on the trade date 16 on February 2011. The data is obtained from Indonesian Stock Exchange (IDX). The parameter estimation is done by ordinary least square. The optimation function for its estimation is done by Nelder Mead simplex. Yield curve on day 16 depicted upward sloping.
Keywords : Government Bond, Yield Curve, Fixed Rate, Nelson Siegel Svensson, Nelder Mead Simplex
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2504
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18109
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI
Jatmiko, Yogo Aryo
Badan Pusat Statistik https://www.linkedin.com/in/yogo-aryo-jatmiko-45ba7786/
Padmadisastra, Septiadi
Universitas Padjajaran
Chadidjah, Anna
Universitas Padjajaran
The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18109
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
eng
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2526
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
PERSEPSI DUNIA KERJA TERHADAP LULUSAN FRESH GRADUATE S1 MENGGUNAKAN MULTIDIMENSIONAL UNFOLDING (Studi Kasus: Dunia Usaha di Kabupaten Batang)
Adhyaksa, M. Atma
Rusgiyono, Agus
Corporate perception of college graduates S1 fresh graduate is a viewpoint held by the world of work regarding the criteria considered in selecting candidates for most job applicants from graduates S1 fresh graduate based on his resume. Perceptions are reviewed based on the configuration of unfolding multidimensional mapping between business entities with the most preferred criteria in the selection process beginning college graduates S1 fresh graduate. Multidimensional unfolding was one of the techniques used in analyzing the proximity between objects is visualized in graphical form in which individuals and stimuli presented in one graph. The result is a grade point average, ability and suitability of computer applications course with the working position is most noticed by companies in selecting a job application letter from the college graduates S1 fresh graduate.
Keywords: Corporate Perception, Fresh Graduate, Multidimensional Unfolding
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2526
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/21173
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)
Utami, Herni
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Sari, Yunita Wulan
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Subanar, Subanar
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Abdurakhman, Abdurakhman
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Gunardi, Gunardi
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Forecasting model; Singular Spectral Analysis; Linear Recurrent Formula; Electricity demand
This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/21173
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/26438
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
ANALYSIS OF SRONDOL-JATINGALEH TOLL QUEUE SYSTEM AT SEMARANG CITY IN THE END OF YEAR 2018 WITH AUTOMATIC TOLL GATE SYSTEM USING LOGISTIC DISTRIBUTION APPROACH
Sugito, Sugito
Statistics Department, Diponegoro University https://scholar.google.co.id/citations?user=wFQ9oyEAAAAJ&hl=id
Prahutama, Alan
Statistics Department, Diponegoro University
Queue system; Logistic Distribution; Srondol-Jatingalih Toll
The transportation sector is one sector that plays an essential role in economic growth. The transportation sector can increase economic growth. Semarang City is one of the provincial capitals in Central Java. The Srondol-Jatingaleh toll road is one of the toll roads in the city of Semarang that has implemented the Automatic Toll Gate. Based on the results of the analysis, so that the queue model is (logistic/logistic/ 4) :( FIFO / ∞ / ∞). It shows that the distribution of the queuing system of the number of arrivals and the number of vehicle services are Logistic-Distribution. The number of service facilities is 4, the service discipline used is First In First Out (FIFO), the size in the queue, and the source of calls are unlimited.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/26438
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
eng
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/22998
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
[RETRACTED] COMBINATION OF SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) AND BACKPROPAGATION NEURAL NETWORK TO CONTRACEPTIVE IUD PREDICTION
Mustaqim, Mustaqim
Badan Kependudukan dan Keluarga Berencana Provinsi Jawa Tengah
Warsito, Budi
Department of Statistics, Diponegoro University
Surarso, Bayu
Department of Mathematics, Diponegoro University
SMOTE; Backpropagation Neural Network; Predict; IUD
[RETRACTED] Data imbalance occurs when the amount of data in a class is more than other data. The majority class is more data, while the minority class is fewer. Imbalance class will decrease the performance of the classification algorithm. Data on IUD contraceptive use is imbalanced data. National IUD failure in 2018 was 959 or 3.5% from 27.400 users. Synthetic minority oversampling technique (SMOTE) is used to balance data on IUD failure. Balanced data is then predicted with neural networks. The system is for predicting someone when using IUD whether they have a pregnancy or not. This study uses 250 data with 235 major data (not pregnant) and 15 minor data (pregnant). From 250 data divided into two parts, 225 training and 25 testing data. Minority class on training data will be duplicated to 1524%, so that the amount of minority data become balanced with the majority data. The results of predictive with an accuracy rate of 99.9% at 1000 epoch.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22998
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/22998/91233
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4835
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
INFERENSI STATISTIK DARI DISTRIBUSI NORMAL DENGAN METODE BAYES UNTUK NON-INFORMATIF PRIOR
Prahutama, Alan
Sugito, Sugito
Rusgiyono, Agus
One of the method that can be used in statistical inference is Bayesian method. It combine sample distribution and prior distribution to get a posterior distribution. In this paper, sample distribution used is univariate normal distribution. Prior distribution used is non-informative prior. Determination technique of non-informative prior use Jefrrey’s method from univariate normal distribution. After got the posterior distribution, find the marginal distribution of mean and variance. So that will get the parameter estimation of interval for mean and variance. Hypothesis testing for mean and variance can find from parameter estimation of formed interval.
Keywords: Bayesian method, non-informatif prior, Jeffrey’s method, Parameter Estimation of Interval, Hypothesis test
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4835
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/37870
2021-07-01T08:52:31Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
MULTIPLE IMPUTATION FOR ORDINARY COUNT DATA BY NORMAL DISTRIBUTION APPROXIMATION
Siswantining, Titin
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia https://scholar.google.co.id/citations?hl=id&user=dyUfiXMAAAAJ https://orcid.org/0000-0001-5160-0020
Ihsan, Muhammad
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Soemartojo, Saskya Mary
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Sarwinda, Devvi
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Al-Ash, Herley Shaori
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
Sari, Ika Marta
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
count data; generelized linear model; missing value; multiple imputation; poisson regression; rubin’s rule.
Missing values are a problem that is often encountered in various fields and must be addressed to obtain good statistical inference such as parameter estimation. Missing values can be found in any type of data, included count data that has Poisson distributed. One solution to overcome that problem is applying multiple imputation techniques. The multiple imputation technique for the case of count data consists of three main stages, namely the imputation, the analysis, and pooling parameter. The use of the normal distribution refers to the sampling distribution using the central limit theorem for discrete distributions. This study is also equipped with numerical simulations which aim to compare accuracy based on the resulting bias value. Based on the study, the solutions proposed to overcome the missing values in the count data yield satisfactory results. This is indicated by the size of the bias parameter estimate is small. But the bias value tends to increase with increasing percentage of observation of missing values and when the parameter values are small.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/37870
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8290
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
PENGELOMPOKAN DAERAH PENGHASIL BAHAN DASAR TEPUNG KOMPOSIT DI INDONESIA MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS (LCCA)
Budiati, Shinta
Susanto, Irwan
Wibowo, Supriyadi
Wheat as a base substance of flour, is a source of carbohydrate which is most used for the manufacturing of variety of foodstuffs. Substitution a part of flour with composite flour for manufacturing food will decrease dependency of imported wheat.This research aims to classify the area which produce base substance of composite flour in Indonesia.For this research we will know a group of provinces which become center of production and development target of local resources potency. One way that is used to grouping the object is cluster analysis. In development, there is another grouping technique used, namely Latent Class Cluster Analysis (LCCA).The results show that the selected model from grouping using LCCA is 3groups. The first group is the enough potential area as a production development center. While the second group have the greatest potential area. Meanwhile the last group is the less potentially area.
Keywords: Composite Flour, Cluster Analysis, Latent Class Cluster Analysis (LCCA)
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8290
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/37445
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL
Yulita, Tiara
Actuary Study Program, Sumatera Institute of Technology
Effendie, Adhitya Ronnie
Mathematics Study Program, Gadjah Mada University
IBNR; RBNS; RDC; Gamma Generalized Linear Models; Reserves
Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimating claims reserves using RDC with background variable is not stable because many combinate of calculation from each background variable. The purpose of this study is to overcome these problems, which we can combine RDC and Gamma Generalized Linear Model (GLM) as an effective method for estimating claims reserves. By using Bootstrapping Individual Claims Histories (BICH) method, the results show that estimation of claims reserves using RDC and Gamma GLM gives the fewest value of Mean Square Error of Prediction (MSEP) rather than RDC with Poisson GLM, RDC, and Chain Ladder. Where the smaller the value of the resulting MSEP estimate, the closer to the actual claim reserve value.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/37445
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10081
2018-02-27T10:10:24Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS
Haqiqi, Baiq Nurul
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik (STIS)
Kurniawan, Robert
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik (STIS)
Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCM’s disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices.
Keywords : Clustering, Fuzzy C-Means, Subtractive Clustering, Subractive Fuzzy C-Means
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10081
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/56798
2024-02-26T03:51:54Z
media_statistika:ART
nmb a2200000Iu 4500
"240226 2024 eng "
2477-0647
1979-3693
dc
APPLICATION OF DELTA GAMMA (THETA) NORMAL APPROXIMATION IN RISK MEASUREMENT OF AAPL'S AND GOLD'S OPTION
Sulistianingsih, Evy
Department of Mathematics, Universitas Tanjungpura, Jl. Prof. Dr. H. Hadari Nawawi, Pontianak, Indonesia 78124 https://orcid.org/0000-0002-7133-1822
Martha, Shantika
Department of Mathematics, Universitas Tanjungpura, Jl. Prof. Dr. H. Hadari Nawawi, Pontianak, Indonesia 78124 https://orcid.org/0000-0001-6124-8534
Andani, Wirda
Department of Mathematics, Universitas Tanjungpura, Jl. Prof. Dr. H. Hadari Nawawi, Pontianak, Indonesia 78124 https://orcid.org/0000-0002-2210-8253
Umiati, Wiji
Department of Mathematics, Universitas Tanjungpura, Jl. Prof. Dr. H. Hadari Nawawi, Pontianak, Indonesia 78124
Astuti, Ayu
Department of Mathematics, Universitas Tanjungpura, Jl. Prof. Dr. H. Hadari Nawawi, Pontianak, Indonesia 78124
Nonlinear-VaR; Derivative; Option-Greek
The option value has a nonlinear dependence relationship on risk factors existing in the capital market. Therefore, this paper considered utilizing Delta Gamma (Theta) Normal Approximation (DGTNA) as a nonlinear approach to determine the change of profit/loss of a European call option to assess the option risk. The method uses the second order of Taylor Polynomial around the stock price underlying the option to approximate the option profit/loss, which is crucial to construct the VaR based on DGTNA. VaR based on DGTNA also considered three Greeks, namely Delta, Gamma, and Theta, known as sensitivity measures in option. This research applied VaR based on DGTN approximation to analyze the European call option of Apple Inc (AAPL) and Barrick Gold Corporation (GOLD) for several strike prices. The performance of DGTN VaR analyzed by Kupiec Backtesting summarized that in this case, DGTN VaR provides the best risk assessment over different confidence levels (80, 90, 95, and 99 percent) compared to Delta Normal VaR and Delta Gamma Normal VaR.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-12-22 11:43:06
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/56798
MEDIA STATISTIKA; Vol 16, No 2 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/44493
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
THE GGE BIPLOT ON RCIM MODEL FOR ASSESSING THE GENOTYPE-ENVIRONMENT INTERACTION WITH SIMULATING OUTLIERS: ROBUSTNESS IN R-SQUARED PROCRUSTES
Hadi, Alfian Futuhul
Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Jember. Jl. Kalimantan No.37 Jember 68121, Indonesia https://orcid.org/0000-0003-2471-1838
Sa'diyah, Halimatus
Biometrics and Plant Breeding Laboratory, Department of Agronomy, University of Jember, Jl. Kalimantan No.37 Jember 68121, Indonesia https://orcid.org/0000-0001-9488-8108
Wicaksono, Dimas Bagus Cahyaningrat
Faculty of Public Health, University of Jember, Jl. Kalimantan No.37 Jember 68121, Indonesia https://orcid.org/0000-0002-8148-7433
GGE Biplot; AMMI; RCIM; outliers; Procrustes
The genotype by environment interaction (GEI) analysis was usually done by Additive Main Effects and Multiplicative Interaction (AMMI) model with Biplot features, and recently there was a Row Column Interaction Model (RCIM) alternatively. In the Biplot of genotype (G) and genotype by environment (GE) interactions, known as the GGE Biplot, the main effect of environment (E) was deleted, while the main effect of G and the interaction effect of GE is kept and combined. Subsequently, continuing our recent research of the robustness of the GGE Biplot in RCIM models, this paper aims to develop the GGE Biplot by RCIM model to analyze the GEI with outlying observations. We used the RCIM model with Asymptotic Laplace Distribution (ALD) that was applied on the simulated data with scattered and single environment outliers to evaluate the robustness of the GGE Biplot. In addition, the robustness was evaluated using the R-squared statistic of the Procrustes analysis. It is shown that the GGE Biplot of RCIM with the ALD family function provides better robustness than the Gaussian. A noticeable superiority of the GGE Biplot with RCIM ALD appeared as the percentage of single environment outliers reach the number of rows of the data matrix.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/44493
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/44493/138132
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13134
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
KAJIAN AKTIVITAS EKONOMI LUAR NEGERI INDONESIA TERHADAP PERTUMBUHAN EKONOMI INDONESIA PERIODE 1998-2014
Hawari, Ryan
Sekolah Tinggi Ilmu Statistik
Kartiasih, Fitri
Sekolah Tinggi Ilmu Statistik
Indonesia is a developing country which adopts an “open economic”. That caused Indonesia economic is strongly influenced by factors that come from outside of Indonesia. External factors in this research is referred to foreign debt, foreign direct investment, trade openness and exchange rate of rupiah with USD. The analytical method in this research used Vector Error Correction Model (VECM) which will focused on Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). Based on result of IRF, exchange rate had a positive effect to economic growth, while foreign debt, foreign direct investment and trade openness had a negative effect to economic growth. Based on result of FEVD, shock on economic growth in Indonesia affected by economic growth itself (43.21%), followed by foreign debt (26.30%), trade openness (14.16%), foreign direct investment (8.29%) and exchange rate (8.04%)
Keywords: economic growth, trade openness, VECM, IRF, FEVD
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13134
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2465
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION
Yasin, Hasbi
Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method.
Keywords: Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2465
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18403
2018-04-08T20:45:28Z
media_statistika:FMT
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18403
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2499
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
PENGUKURAN VALUE AT RISK PADA ASET TUNGGAL DAN PORTOFOLIO DENGAN SIMULASI MONTE CARLO
Maruddani, Di Asih I
Purbowati, Ari
Value at Risk (VaR) is the established standard for measuring market risk. VaR measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level. A VaR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). The Monte Carlo simulation method calculates the change in the value of positions by using a random sample generated by price scenarios. Instead of using the past value of risk factors, Monte Carlo simulation generates models to estimate the risk factors from past portfolio returns by specifying the distributions and their parameters. Using these distributions and parameters, we can generate thousands of hypothetical scenarios for risk factors and, finally, we can determine future prices or rates based on hypothetical scenarios. VaRs can be derived from the cumulative distribution of future prices or rates for given confidence levels. In this paper, we calculate VaR at PT Astra International Tbk., PT Telekomunikasi Tbk., and the portfolio of the two assets. PT. Astra International Tbk has higher VaR than PT. Telekomunikasi Tbk. The VaR of a portfolio has lower result than VaR of each single asset.
Keywords : Value at Risk, Time Period, Confidence Level, Monte Carlo Simulation.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2499
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20591
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
Widiharih, Tatik
Mukid, Moch Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Credit Scoring is designed so that lenders can easily make decisions regarding whether a loan proposal from a prospective customer is worthy of approval or not. This study examines the application of the Multi Local Means Based K Harmonic Nearest Neighbor (MLMKHNN) method in the case of motorcycle credit in a financial institution. The classification capability of this method in detecting potential borrowers into the credit category is either good or bad compared to its previous method, Local Means Based K Harmonic Nearest Neighbor (LMKNN). In this case the MLMKHNN method has not shown better performance than the LMKNN method. At the same level of total accuracy, MLMKHNN requires more numbers of neighbors than the number of neighbors required by the LMKNN method.
Keywords: sampling design, all possible samples, statistical efficiency, cost efficiency
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20591
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
ind
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2519
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
PEMODELAN REGRESI PROSES GAUSSIAN PEMODELAN REGRESI PROSES GAUSSIAN MENGGUNAKAN FUNGSI PERAGAM EKSPONENSIAL KUADRAT
Mukid, Moch. Abdul
Gaussian Process is a collection of random variables where any finite subset of that has a joint multivariate Gaussian distribution. A Gaussian Process is fully specified by its mean and its covariance function. One of the popular covariance functions is squared exponential that has two hyperparameters. In this paper Gaussian Process is used to made a prediction of the number of clothes produced by PT. APAC INTI CORPORA based on the number of attending employes, the number of overtime employes, the number of brokendown machines and used materials.
Keywords: Gaussian Process, Covariace Functions, Squared Exponential
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2519
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/25499
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA)
Maulina, Retsi Firda
Badan Pusat Statistik
Djuraidah, Anik
Departemen Statistika, FMIPA, IPB University
Kurnia, Anang
Departemen Statistika FMIPA, IPB University
Bayesian; INLA; Poverty; Probit; Spatial
Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/25499
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4523
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
ANALISIS TINGKAT STRESS WANITA KARIR DALAM PERAN GANDANYA DENGAN REGRESI LOGISTIK ORDINAL (Studi Kasus pada Tenaga Kerja Wanita di RS. Mardi Rahayu Kudus)
Nova, Nova
Ispriyanti, Dwi
Currently, the role of women has shifted from traditional to modern roles. From only a traditional role to bear children and run the household, women now have a social role which can be a career with supported higher education. This can result in conflict dual role as worker and housewife for women who have a family, so easy to cause stress. Several factors are thought to affect levels of stress, especially for career women is child care, housekeeping assistance, communication and interaction with children and husband, time for family, determining priorities, career pressures and family pressures, and the husband's view of the dual role of women. Based on the test independence of variables, seven variables have a relationship with the level of stress career woman.By using the likelihood ratio test and Wald test is found to be two factors affect the stress levels of women workers in Mardi Rahayu Kudus hospital are a time for family and the support of her husband in a career.
Keywords: Stress Level, Dual Role Conflict, Ordinal Logistic Regression, Mardi Rahayu Hospital.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4523
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/29613
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING
Hermansah, Hermansah
Mathematics Education Study Program, Riau Kepulauan University
Rosadi, Dedi
Mathematics Study Program, Gadjah Mada University
Abdurakhman, Abdurakhman
Mathematics Study Program, Gadjah Mada University
Utami, Herni
Mathematics Study Program, Gadjah Mada University
Stepwise Method; Learning Method; Activation Function; Ensemble Operator; NARNN Model
NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/29613
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/26612
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
GEOGRAPHICALLY WEIGHTED PANEL REGRESSION WITH FIXED EFFECT FOR MODELING THE NUMBER OF INFANT MORTALITY IN CENTRAL JAVA, INDONESIA
Rusgiyono, Agus
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University https://scholar.google.co.id/citations?user=XEYQbuMAAAAJ&hl=id
Prahutama, Alan
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University
Geographycally Weighted Panel Regression; Fix Effect; Infat Mortality
One of the regression methods used to model by region is Geographically Weighted Regression (GWR). The GWR model developed to model panel data is Geographically Weighted Panel Regression (GWPR). Panel data has several advantages compared to cross-section or time-series data. The development of the GWPR model in this study uses the Fixed Effect model. It is used to model the number of infant mortality in Central Java. In this study, the weighting used by the fixed bisquare kernel resulted in a significant variable percentage of clean and healthy households. The value of R-square is 67.6%. Also in this paper completed by spread map base on GWPR model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/26612
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7642
2016-03-15T17:19:31Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
ANALISIS OBYEK DAN KARAKTERISTIK DARI MATRIKS INDIKATOR MENGGUNAKAN HYBRID ANALISIS KELAS LATEN DENGAN BIPLOT ANALISIS KOMPONEN UTAMA (BIPLOT AKU)
Ginanjar, Irlandia
Pravitasari, Anindya Apriliyanti
Martuah, Aleknaek
Analysis of the object and the characteristics will be much easier, efficient, and informative when based on a perceptual map, which can display objects and characteristics. Indicator matrix is a matrix where the rows represent objects and the columns is a dummy variable representing characteristics. This article writes about techniques to make perceptual map from indicator matrix, where that can provide information about the similarity between objects, the diversity of each characteristic, correlations between the characteristics, and characteristic values for each object, the techniques we call Hybrid Latent Class Cluster with PCA Biplot, where Latent Class Cluster Analysis is used to transform the indicator matrix to cross section matrix, where rows represent the objects and columns represent the characteristics, the observation cells is the probability of characteristic for each object, next the cross section matrix mapped using Principal Component Analysis Biplot (PCA Biplot).
Key Words: Hybrid Latent Class Cluster with PCA Biplot, Latent Class Cluster Analysis, Biplot Principal Component Analysis, Indicator Matrix.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7642
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/39369
2022-01-12T02:14:22Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION
Hoyyi, Abdul
Department of Mathematics, Gadjah Mada University
Department of Statistics, Diponegoro University https://orcid.org/0000-0002-3393-0787
Abdurakhman, Abdurakhman
Department of Mathematics, Gadjah Mada University
Rosadi, Dedi
Department of Mathematics, Gadjah Mada University
Stochastic process; Black-Scholes-Merton; Le ̀vy process; Variance Gamma; Monte Carlo simulation
The Option is widely applied in the financial sector. The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the process exponential model. The process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK). The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various values until convergent result was obtained.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/39369
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9199
2018-02-27T10:48:41Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
MODEL CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN ESTIMASI MOMENT PROBABILITAS TERBOBOTI
Rusgiyono, Agus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Wuryandari, Triastuti
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Rahmawati, Annisa
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The methods is used to analyze extreme rainfall is the Extreme Value Theory (EVT). One of the approaches of EVT is the Block Maxima (BM) which it follows the distribution of Generalized Extreme Value (GEV). In this study, the dasarian rainfall data of 1990-2013 in the Semarang City is divided based on block monthly and examined in October, November, December, January, February, March and April. The resulted blocks are 24 with 3 observations each block. Parameter shape, location and scale are estimated Probability Weight Moments (PWM) methodes The result of this study are January has the greatest occurrence chance of extreme value, estimated of parameter shape 0,3840564, location 138,8152989 and scale 68,6067117. In addition, the alleged maximum value of dasarian rainfall obtained in a period of 2, 3, 4, 5 and 6 years are 243,45753 mm, 308,23559 mm, 357,26996 mm, 397,96557 mm and 433,28889 mm respectively.
Keywords: Rainfall, Extreme Value Theory, Block Maxima, Generalized Extreme Value, Probability Weight Moments
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9199
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/55966
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231212 2023 eng "
2477-0647
1979-3693
dc
COMPARISON OF SPATIAL WEIGHTED MATRIX BETWEEN POWER AND QUEEN ON THE SPATIAL EMPIRICAL BEST LINEAR UNBIASED PREDICTION MODEL (Study on Per Capita Expenditure in East Java Province in 2019)
Amaliana, Luthfatul
Department of Statistics, Brawijaya University
Prasetya, Andi
Department of Statistics, Brawijaya University
Spatial Analysis; Indirect Estimation; Queen; Power; Expenditure Per Capita; Small Area Estimation
This study aims to make a comparison related to the spatial weighted matrix of power and queen in the SEBLUP model to estimate per capita expenditure in East Java in 2019. The data used is secondary data then the data were analyzed by the Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). The results of this study indicate that the best spatial weighted matrix for estimating per capita expenditure in East Java using the SEBLUP model is the spatial weighted matrix of Queen, because it produces the smallest MSE value. In this study, the factors that significantly affect East Java's per capita expenditure are population density (X1), number of health facilities (X2), number of public elementary schools (X3), and the percentage of residents who have BPJS as the Fund Assistance Recipients (X5). The novelty of this study are combining multiple determinant factors that have demonstrated their substantial/significant effect on the average per capita expenditure and focusing on the regions characters in intermediate size (16<n<64).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/55966
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/45661
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
MODELING THE CONTRIBUTION OF THE MANUFACTURING SECTOR TO THE GROSS DOMESTIC PRODUCT OF KENYA USING TIME SERIES ANALYSIS
Wanyonyi, Maurice
Department of Mathematics and Statistics, University of Embu, Kenya https://orcid.org/0000-0002-5894-2549
Gross Domestic Product; Autoregressive Integrated Moving Average model; inflation rate; manufacturing sector.
The manufacturing sector is considered a pivotal contributor to the growth of the economy around the globe. Kenya relies on the manufacturing sector to generate revenue and ultimately enhance the growth of the economy. Despite the key purpose played by these sectors in the economy, inflation rate has diversely affected their performance. The purpose of the study was to develop the Autoregressive Integrated Moving Average time series model to forecast the inflation rate in Kenya. The analysis utilized secondary data from the Kenya National Bureau of Statistics and the model was fitted to the data using R. The ARIMA with the information criterion of 576.24 was identified as the best model. Based on the forecasting, it was established that there will be a slight shift in the inflation in the coming years. Therefore, the government should use wage and price control to fight inflation but put in place policies to prevent recession and job loss in the country. The government should also employ contractionary monetary policy to fight inflation by reducing the money supply in the economy through decreases bond prices and increased interest rates. Implementation of these recommendations might assist in reducing the rate of inflation in the country.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45661
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11724
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
PEMODELAN GRAFIK PENGENDALI TOTAL DAN RATAAN DISKRIT UNTUK GENERALISASI DISTRIBUSI GEOMETRIK
Sudarno, Sudarno
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Mukid, Moch. Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Total events which do by counting will be obtained discret data type. The discret data type and geometric distribution could be drawn by total number of events chart (G chart) and average number of event chart (H chart). In this research result upper control limit, center line, and lower control limit, both G chart and H chart. Data processing of the case, resulting G chart that upper control limit is 80.77 and center line is 39.8, meanwhile by H chart obtained that upper control limit and center line, respectively, 11.54 and 5.8. The results of G chart and H chart could be used for prediction events at the future to anticipate the real problems. Therefore, the systems have no problem and their activities will be dynamic, stable and best perform.
Keywords:Geometric Distribution, Total Number of Event Chart, Average Number of Event Chart
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11724
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18273
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
PELUANG ALUMNI PENDIDIKAN MATEMATIKA FKIP UMB DALAM MENDAPATKAN PEKERJAAN DENGAN MENGGUNAKAN ANALISIS REGRESI LOGISTIK
Mahyudi, Mahyudi
Program Studi Pendidikan Matematika, Universitas Muhammadiyah Bengkulu
Graduation or college graduation become the most exciting moment for a student. In addition to successfully get a degree, they are also eager to enter the workforce. But sometimes the spirit was lost in the middle of the road. Many fresh graduates complain of difficult to get a job at this time. Every year the number of graduates to grow while jobs are not directly proportional to the increase in the number of graduates. The study analyzed what are the chances of graduates Mathematics Education FKIP Muhammadiyah University of Bengkulu in getting a job. Samples taken as many as 78 graduates between September 2015 to April 2016. The factors considered were gender, age, GPA, national origin, jobs for college and the work areas as desired. Analysis of survey data using ordinal logistic regression analysis. The results showed that the dominant factors that affect the length of the graduates in getting a job is GPA, work experience in college and the desired field of work.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18273
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2482
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
ANALISIS KORELASI KANONIK PADA PERILAKU KESEHATAN DAN KARAKTERISTIK SOSIAL EKONOMI DI KOTA PATI JAWA TENGAH
Safitri, Diah
Indrasari, Paramita
One of the general problem that is social and economics which not yet flatten and still meeting of low health case. To know the correlation between social and economics characteristic and behavior of health in Pati of Central Java is used the canonical correlation analysis. The variable that is taken are social and economics characteristic and behavior of health variable, which each consisting of nine indicator variable. Some assumptions like linearity, normal multivariable and do not multicolinearity should be fulfilled. After the assumption have fulfilled, data processing can be done so that obtained a conclusion. The result of canonical correlation analysis indicate that there is a signifikan correlation between social and economics characteristic variable and behavior of health variable. From nine indicator which forming variable of social and economics characteristic, earnings indicator variable, education of mother, expenditure and education of father giving the most dominant of contribution. While from nine behavior of health variable, indicator of balanced nutrient variable, physical activity, eradication of mosquito den, house floor, exclusive ASI, and brush teeth giving the most dominant of contribution.
Keywords : Social and Economics Characteristic, Behavioral of Health, Canonical Correlation Analysis
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2482
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/16544
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
ANALISIS KEPUTUSAN NASABAH DALAM MEMILIH JENIS BANK: PENERAPAN MODEL REGRESI LOGISTIK BINER (STUDI KASUS PADA BANK BRI CABANG BALIKPAPAN)
Ghozi, Saiful
Program Studi Perbankan dan Keuangan, Politeknik Negeri Balikpapan https://scholar.google.co.id/citations?user=cE-5BqwAAAAJ&hl=en&oi=ao
Ramli, Ramli
Program Studi Perbankan dan Keuangan, Politeknik Negeri Balikpapan
Setyani, Asri
Program Studi Perbankan dan Keuangan, Politeknik Negeri Balikpapan
This paper analyze factors that influence customer preference between conventional and sharia bank, and which factor is the most dominant. The study was conducted in Balikpapan city from May 2017 until August 2017. The sample is 25 customers of BRI Sharia and 31 customers of conventional BRI. Statistical analysis model used in this paper is Binary Logistics Regression. There are 8 predictor variables to be analyzed to know their effect to customer decision in choosing bank between sharia bank and conventional bank. The variables are: knowledge of respondents about sharia bank (X1), knowledge of respondents about the difference between conventional and sharia banks (X2), knowledge of respondents about products offered by sharia bank (X3), promotion of sharia bank via printed media (X4), promotion of sharia bank via electronic media (X5), promotion of sharia bank in social activities (X6), the customer's efforts to observe religious orders (X7), and the customer's efforts to avoid the religious prohibitions (X8). The results of individual significance test indicate that knowledge of respondents about sharia bank, and promotion of sharia bank through electronic media has significant effect to the customer’s decision in choosing bank. And the most significant effect is promotion through electronic media (X5).
Keywords : binary logistic regression, decision, sharia bank
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/16544
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16544/40296
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2510
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
UJI HIDUP DIPERCEPAT PADA DISTRIBUSI EKSPONENSIAL TERSENSOR TIPE II DENGAN TEGANGAN KONSTAN
Prayudhani, Oktaviana
Wuryandari, Triastuti
Accelerated Life Testing (ALT) is used to obtain information quickly on life distribution, failure rates and reliabilities. ALT is achieved by subjecting the test units to conditions such that the failure occur sooner. Prediction of long term reliability can make within a short periode of time. Result from the ALT are used to extrapolate the unit characteristic at any future time and at given normal operating conditions. ALT using a time varying stess application is often used to induce failure in relatively short times. The most basic and useful type of ALT in which the stress on each unit is increased step by step over time, it can substantially shorten the duration of the reliability test. The life distribution which used in reliability test is exponential distribution. By using Maximum Likelihood Estimation is obtained point estimation of parameter on step stress, and povital quantity is obtained confidence interval for parameter. From this estimation Mean Time to Failure (MTTF) and reliability of product under normal operating condition
Keysword: Accelerated Life Testing (ALT), Step Stress, Exponential Distribution, Maximum Likelihood Estimation, Povital Quantity
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2510
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/23101
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
METODE DIAGONALLY WEIGHTED LEAST SQUARE (DWLS) PADA STRUCTURAL EQUATION MODELLING UNTUK DATA ORDINAL: STUDI KASUS DARI PENGGUNA JASA KERETA API MAJAPAHIT MALANG – PASAR SENEN
Isnayanti, Isnayanti
Departemen Matematika, Universitas Gadjah Mada
Abdurakhman, Abdurakhman
Departemen Matematika, Universitas Gadjah Mada
Structural Equation Modelling (SEM) is used to examine the relationship between complex variables to obtain a comprehensive picture of the overall model. The basic assumptions in SEM are continuous data types and multivariate normality distributed. But in some studies on social sciences, educational sciences, and medical sciences, the data used usually comes from ordinal variables in the form of a Likert scale which causes data to be not multivariate normal distribution. Diagonally Weighted Least Square (DWLS) is one method that can be used to overcome this problem. In this paper, ordinal data analysis will be conducted on SEM using polychoric correlation data with the DWLS method to compare the results of the suitability of the model with the Maximum Likelihood (ML) method. The discussion is complemented by a case study of the effect of service quality on customer satisfaction and loyalty of Majapahit Railway service in Malang-Pasar Senen.The results showed that the proposed model fit after modification model based on the criteria of 'goodness of fit' with chi-square value T=15.24, P-value=0.5785, RMSEA=0.000, GFI=0.99, AGFI=0.97, NNFI =1.03, CFI=1.00, PNFI=0.53.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/23101
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2629
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
MODEL PERTUMBUHAN DUA SAMPEL
Sudarno, Sudarno
Growth curve model is generalization of the multivariate regression models. This paper determine growth curve model at two samples of ras. They are observed body weight after given by substances. The problems will be done are testing for an outlier, testing for multivariate normality, testing for hogeneous, test of the adequacy of the model, and estimate of the parameters model. In doing computation and visualization of statistics at needed formulas, use up date some softwares, such as SAS, Minitab, MATLAB, etc. If the estimated model is known, it can be used to some objectives. The estimate model could be applied to predicted tool for next time, give characteristic and properties of the model. The growth of rat which be given thyroxin substance, at beginning time cause significant increasing of the body weight, but after at sixth weeks, given substance imply significant decreasing of their body weight, too. Meanwhile for the rat be given thyouracil, at beginning time to tenth weeks, their body weight affect slowly increasing by continue. Therefore thyroxin substance should be given before seventh weeks, but for thyouracil substance could be given continuosly if cause increasing the body weight. This result could be used to optimalization of rat growth.
Key words: Multivariate normal test, Adequacy of model test, Growth curve model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2629
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22804
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
ANALISIS CURAH HUJAN BULANAN DI KOTA AMBON MENGGUNAKAN MODEL HETEROSKEDASTISITAS: SARIMA-GARCH
Sinay, Lexy Janzen
Jurusan Matematika, Universitas Pattimura http://matematika.fmipa.unpatti.ac.id/l-j-sinay/ http://orcid.org/0000-0001-6311-8354
Lembang, Ferry Kondo
Jurusan Matematika, Universitas Pattimura
Aulele, Salmon Notje
Jurusan Matematika, Universitas Pattimura
Mustamu, Dominique
Jurusan Matematika, Universitas Pattimura
Ambon City; Heteroscedasticity; Rainfall; SARIMA-GARCH volatility clustering
Non-linear characteritics in rainfall allow volatility clustering. This condition occurs in Ambon City with seasonal rainfall patterns. The aims of this research are to find the best model and to forecast monthly rainfall in Ambon City using heteroscedasticity model. This research examines secondary data from BMKG for monthly rainfall data in Ambon City from January 2005 – December 2018. The data is divided into two parts. First part, is called in-sample data, consist of data form January 2005 – December 2017. Second part, is called out-sample data, consist data from Januari 2018 – December 2018. The research used SARIMA–GARCH to model the data. The results are the is the best model and the residual model satisfied assumptions of normality, white noise, and there is no ARCH effect. The MAPE value in simulation using in-sample data is 0.73%. On the other side, the MAPE value of forecast results is 30%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22804
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/22804/91188
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/36770
2022-07-27T00:34:42Z
media_statistika:FMT
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
Front Matter Vol. 12 No. 2 2019
Statistika, Media
Cover dan Daftar Isi Media Statistika Vol. 12 No. 2 Desember 2019
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36770
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
eng
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5667
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
PEMETAAN PENYAKIT DEMAM BERDARAH DENGUE DENGAN ANALISIS POLA SPASIAL DI KABUPATEN PEKALONGAN
Yasin, Hasbi
Saputra, Ragil
The number of dengue haemorrhagic fever (DHF) incidence in Pekalongan from year to year is very volatile. In 2006, there was 352 cases, 718 cases occurred in 2007, 2008 saw 403 cases, 2009 there were 753 cases, whereas in 2010 a decline to 223 cases. This is possible due to the lack of information about the place, time and location of the incident spread of dengue in Pekalongan. Various efforts have been made to address these issues both society and government but the incidence of this disease has not been effectively suppressed. The results of data analysis showed that the incidence of dengue in Pekalongan mostly occurs during the rainy season is the period from January to June. The DHF incidence tends to be higher in Kedungwuni. Highest incidence of DHF occurred in April 2010. In addition, there are some months that indicate the spatial relationships in the incidence of dengue in Pekalongan, ie January, February, July, October and December. The sub-district that has a positive autocorrelation is Kedungwuni, Wonopringgo, and Tirto. While the sub-district has a negative autocorrelation is Karangdadap. Most of the sub-districts in Pekalongan status is still endemic for dengue.
Keywords: DHF, Moran’s Index, Spatial Pattern
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5667
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/43699
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)
Tarno, Tarno
Department of Statistics, Diponegoro University https://scholar.google.co.id/citations?user=rSe3L94AAAAJ&hl=id
Trimono, Trimono
Data Science Study Program, UPN Veteran Jawa Timur
Maruddani, Di Asih I
Department of Statistics, Diponegoro University
Wilandari, Yuciana
Department of Statistics, Diponegoro University
Utami, Rianti Siswi
School of Mathematics and Statistics, The University of New South Wales Sidney
Department of Mathematics, Gadjah Mada University
stocks portfolio; loss risk; heteroskedastic; VaR, Backtesting
Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/43699
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8491
2018-02-27T11:00:17Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
RANCANGAN D-OPTIMAL UNTUK MODEL EKSPONENSIAL GENERAL
Widiharih, Tatik
Jurusan Statistika, FSM, Universitas Diponegoro
Haryatmi, Sri
Jurusan Matematika, FMIPA, Universitas Gadjah Mada
Gunardi, Gunardi
Jurusan Matematika, FMIPA, Universitas Gadjah Mada
Exponential model is widely used in biology, chemistry, pharmacokinetics and microbiology. D-optimal criteria is criteria with the purpuse to minimize the variance of the estimator of parameters in the model. In this paper will discuss the D-optimal design for the generalized exponential model with homoscedastics errore assumtion. We used minimally supported design with the proportion of each design point is uniform. The optimization is used modified Newton, and the results obtained that the design points are interior points of the design region.
Keywords: D-Optimal, Generalized Exponential, Minimally Supported Design, Support Point, Homoscedastics
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8491
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/56717
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231107 2023 eng "
2477-0647
1979-3693
dc
GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM
Azizah, Aliyah Husnun
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Nurjannah, Nurjannah
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Fernandes, Adji Achmad Rinaldo
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Hamdan, Rosita
Department of Development Economics, University Malaysia Serawak
Geographically Weighted Regression; Geographically Weighted Panel Logistic Regression Semiparametric; Poverty Gap Index.
Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/56717
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/47209
2022-07-28T02:52:59Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
FUZZY VECTOR AUTOREGRESSION FOR FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE
Nurhayadi, Nurhayadi
Department of Mathematics and Science Education, Tadulako University https://orcid.org/0000-0001-8246-5386
Vector; Autoregression; Fuzzy; Gaussian; Median
Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/47209
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11676
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
TIME SERIES ANALYSIS USING COPULA GAUSS AND AR(1)-N.GARCH(1,1)
Caraka, Rezzy Eko
Awardee of LPDP Scholarship, Ministry of Finance
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Sugiarto, Wawan
Senior High School 1 Moro, Riau Islands Province
Ismail, Kadi Mey
Awardee of LPDP Scholarship, Ministry of Finance
In this case, the Gaussian Copula is used to connect the data that correlates with the time and with other data sets. Most often, practitioners rely only on the linear correlation to describe the degree of dependence between two or more variables; an approach that can lead to quite misleading conclusions as this measure is only capable of capturing linear relationships. Correlation doesn’t mean causation, prediction using Copula is built on three things that the marginal distribution function, the kernel function, and the function of the Copula. Gaussian Copula involves the covariance matrix are approximated by using kernel functions. Kernel acts as the correlation between the approach of the data values that have the same characteristics. In this case, the characteristics used is the time. The advantage of the kernel function is able to calculate the correlation between random variables that have a realization using data characteristics. The advantage of using the kernel based Copula able to capture the dependencies between data and process data that have the same characteristics with time. Another benefit is that it allows a sequence of random variables have a joint distribution function so that the conditional probability of the prediction can be calculated.
Keywords: Binding, Copula, GARCH, Gauss, Time Series
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11676
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/15601
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Metode Nonlinear Least Square (NLS) untuk Estimasi Parameter Model Wavelet Radial Basis Neural Network (WRBNN)
Santoso, Rukun
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Sudarno, Sudarno
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The use of wavelet radial basis model for forecasting nonlinear time series is introduced in this paper. The model is generated by artificial neural network approximation under restriction that the activation function on the hidden layers is radial basis. The current model is developed from the multiresolution autoregressives (MAR) model, with addition of radial basis function in the hidden layers. The power of model is compared to the other nonlinear model existed before, such as MAR model and Generalized Autoregressives Conditional Heteroscedastic (GARCH) model. The simulation data which be generated from GARCH process is applied to support the aim of research. The sufficiency of model is measured by sum squared of error (SSE). The computation results show that the proposed model has a power as good as GARCH model to carry on the heteroscedastic process.
Keywords:
Wavelet, Radial Basis, Heteroscedastic Model, Neural Network Model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15601
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/1158
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
PENELUSURAN KERAGAMAN DALAM BLOK PADA RANCANGAN ACAK KELOMPOK DENGAN INTERGRADIEN
Rahmawati, Rita
Program Studi Statistika FMIPA UNDIP
Gedung Lt. III Program Studi Statistika Jurusan Matematika
FMIPA Universitas Diponegoro
Jl. Prof. H. Soedarto, SH, Tembalang, Semarang 50255
Dalam Rancangan Acak Kelompok Lengkap (RAKL), asumsi terpenting adalah unit percobaan dalam blok harus bersifat homogen. Asumsi ini sulit dipenuhi jika ukuran blok terlalu besar. Penelitian ini bertujuan untuk menelusuri keragaman yang ada dalam blok dengan memasukkan unsur arah keragaman (baris atau kolom dalam blok) yang mungkin ada, sehingga analisis ragam yang kemudian dihasilkan akan memberikan keragaman galat yang lebih kecil. Penelusuran keragaman dengan cara ini disebut analisis Intergradien. Dalam penelitian ini digunakan data jumlah anakan produktif/rumpun yang diperoleh dari Balai Tanaman Padi Sukamandi dalam Penelitian Interaksi antara Genotipe dengan Lingkungan Galur Harapan Padi Sawah pada Agroklimat Utama. Hasil dari penelitian ini, juga dengan data simulasi, memberi kesimpulan bahwa analisis Intergradien dalam RAKL menghasilkan kuadrat tengah galat (KTG) yang lebih kecil daripada RAKL biasa. Tetapi karena unsur baris dan arah keragaman pada data jumlah anakan produktif/rumpun tidak berpengaruh nyata pada alpha 5%, maka digunakan RAKL biasa untuk menentukan varietas terbaik. Dengan RAKL, didapatkan IR71031 memiliki jumlah anakan produktif/rumpun yang paling besar.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/1158
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18409
2018-04-07T21:59:05Z
media_statistika:FMT
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18409
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2505
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
PENGUKURAN RISIKO PADA RETENSI OPTIMAL UNTUK REASURANSI STOP LOSS DENGAN VALUE AT RISK
Sunarwatiningsih, Agustina
Wilandari, Yuciana
Rusgiyono, Agus
Reinsurance is an effective risk management tool for an insurer to minimize the risk of loss. Optimization criteria is based in a minimum VaR of the total risk of in insurer, to derive the optimal retention in stop loss reinsurance. The resulting optimal solution of optimization criterion has several important characteristics, such as: the optimal retention has a very simple analytic form; the optimal retention depends only on the assumed loss distribution and the reinsurer’s loading factor; if optimal solution exist, then VaR based optimization criteria yield the same optimal retentions; there exist a exceeds risk tolerance level which the insurer optimally should not reinsure her risks. The approach allows us to obtain different results of the optimization problem depends on the measurement of risk used. Furthermore, with optimal retention of risk measurement and minimum of VaR to the total risk, the companies be able to minimize or reduce the loss ratio of claims own retention ceding company. One way to show the existence of an optimal retention used survival function distribution exponensial.
Key words: Stop Loss Reinsurance, Optimal Retention, Value at Risk (VaR)
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2505
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/21490
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
PERAMALAN CURAH HUJAN EKSTRIM DI PROVINSI BANTEN DENGAN MODEL EKSTRIM SPASIAL
Djuraidah, Anik
Departemen Statistika, Institut Pertanian Bogor http://sinta2.ristekdikti.go.id/author/?mod=profile&p=stat
Suheni, Cici
Departemen Statistika, Institut Pertanian Bogor
Nabila, Banan
Kementerian Pendidikan dan Kebudayaan
Extreme rainfall can cause negative impacts such as floods, landslides, and crop failures. Extreme rainfall modeling using spatial extreme models can provide location information of the event. Spatial extreme models combine the extreme value theory, the max-stable process, and the geostatistical correlation function of F-madogram. The estimation of the return value on the spatial extreme models is performed using the copula approach. This research used monthly rainfall data from January 1998 until December 2014 at 19 rain stations in Banten Province. The results showed that there was a high spatial dependence on extreme rainfall data in Banten Province. The forecast in range 1.5 years showed the best result compared to other ranges (1 year, 3 years, and 5 years) with MAPE 20%. The pattern of extreme rainfall forecasting was similar to its actual value with a correlation of 0.7 to 0.8. The predicted location that has the highest extreme rainfall was the Pandeglang Regency. Extreme rainfall forecasting at 19 rain stations in Banten Province using spatial extreme models produced a good forecasting.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/21490
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/21490/57310
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2606
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
ESTIMASI MODEL UNTUK DATA DEPENDEN DENGAN METODE CROSS VALIDATION
Tarno, Tarno
This paper discuss about application of cross-validation method for modeling of dependent data. One of the data that categorized into dependent data is a time series. To construct the mathematical model for a time series data, we must have at least 50 series. In practices we often have some problem as long as we collect the time series data. So we don’t get ideal data related to number of sample. To solve this problem, we can generate observation data. There are several methods that can be used to generate data such as cross-validation and bootstrap. Application of cross-validation method to generate time series data can’t be done randomly, but we must generate the data based on balanced incomplete block design. The basic principle of cross-validation method is the data divided into two parts those are construction data and validation data. Construction data are drawn from observation data based on moving block and then we construct the model with Box-Jenkins method and verify the model with validation data. Do this process for different blocks as replication samples of cross-validation method, such that we can construct the best model that minimized loss function for prediction errors.
Key words: time series data, estimate model, cross-validation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2606
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22196
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
PENENTUAN SEBARAN SPASIAL PENCEMARAN AIR DI KOTA PONTIANAK MENGGUNAKAN ANALISIS DISKRIMINAN DUA KELOMPOK
Fikri, Muhammad
Department of Statistics, Tanjungpura University
Debataraja, Naomi Nessyana
Department of Statistics, Tanjungpura University
Kusnandar, Dadan
Department of Statistics, Tanjungpura University
Dependence Method; Pollution Index; Apparent Error Rate; Mapping
Clean water is one point of sustainable Development Goals (SDGs), so to keep indicator water quality must be determine every period. The Discriminant analysis is an analysis of dependence that is used to classify objects into several categories. The purpose of this study were to determine the discriminant model that consist of dominant factors of water pollution. Samples were taken from 42 locations in the surrounding area of Pontianak City. The sample were analyzed in the laboratory for the contents of ferrum (Fe), dissolved oxygen (DO), biochemical oxygen demand (BOD). Width of the river were also considered is an independent variable. The methodology includes determining the pollution index that will be used as dependent variable, testing the assumption of multivariate normality and similarity of the covariance variance, conducting the discriminant analysis classification process using the Apparent Error Rate method. The pollution level of each location was visualized in a map. The resulting discriminant model has an accuracy rate of 69%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22196
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
eng
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/28272
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
A SIMULATION STUDY OF FIXED-B ASYMPTOTIC DISTRIBUTIONS IN LINEAR PANEL MODELS WITH FIXED EFFECTS
Setyowati, Indah Rini
Statistics Department, IPB University
Notodiputro, Khairil Anwar
Statistics Department, IPB University
Kurnia, Anang
Statistics Department, IPB University
Fixed-b asymptotic distribution; Fixed effect; HAC; HACSC; Panel data
In linear models, panel data often violates the assumption that the error terms should be independent. As a result, the estimated variance is usually large and the standard inferential methods are not appropriate. The previous research developed an inference method to solve this problem using a variance estimator namely the Heteroskedasticity Autocorrelation Consistent of the Cross-Section Averages (HACSC), with some improvements. The test statistic of this method converges to the fixed-b asymptotic distribution. In this paper, the performance of the proposed inferential method is evaluated by means of simulation and compared with the standard method using plm package in R. Several comparisons regarding the Type I Error of these two methods have been carried out. The results showed that the statistical inference based on fixed-b asymptotic distribution out-perform the standard method, especially for the panel data with small number of individual and time dimension.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/28272
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/28272/105115
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/28887
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE
Hidayati, Lilik
Departement of Mathematics, Airlangga University
Chamidah, Nur
Departement of Mathematics, Airlangga University http://orcid.org/0000-0003-1592-4671
Budiantara, I Nyoman
Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS)
Confidence Interval Estimation; UNBK Scores; Multi-Response Semiparametric; Truncated Spline
Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) scores in West Nusa Tenggara Province where its UNBK in the lowest position among other provinces in Indonesia. The method used for estimating parameters is weighted least square. The best model is determined based on the Generalized Cross Validation (GCV) minimum value. Based on the estimated 95% confidence interval of parameters of the multi-response truncated spline semiparametric regression model, the results showed that the insignificant factors affecting the UNBK scores were gender and parental education duration while the report card of scores and USBK scores had a positive effect on the UNBK scores but only the UNBK scores of mathematics that report card of scores factor has a negative effect on it.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/28887
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/28887/83681
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/28887/91262
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4836
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
APLIKASI DOE UNTUK MENENTUKAN SETTING PARAMETER OPTIMUM PADA PROSES PEMBUATAN PRODUK ROLL
Anggoro, Paulus Wisnu
Problem faced by Atmaja Jaya Industry, Klaten is how to produce high quality products with a minimal amount of defective products. Therefore, this study will look at factor that affect the quality of roll 6” TL in order to obtain the best level setting in the production process. This study uses experimental design with the Taguchi method. Factors to be tested in this study is long making liquid metal (factor A), the old foundry (factor B), and total time of casting (factor C). Each factor has three levels so that the use of orthogonal array L934. From the result of pooling up mean, the best level combination of factors that affect the quality of roll 6”TL is the length of manufacture of liquid metal which set at 100 minutes, the old foundry is set at 5 seconds and the total time of casting that set in 14 minutes. While the result of pooling up SN ratio for the best level combination of factors that affect the quality of production process variants roll 6”TL which the length of the manufacture of liquid is set at 100 minutes and total time of casting that are set in 14 minutes.
Keywords: Taguchi Method, Orthogonal Array, Roll 6” TL, Product defect
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4836
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/31331
2021-07-01T08:56:38Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA
Kurniawati, Yenni
Universitas Negeri Padang https://www.scopus.com/authid/detail.uri?authorId=57202279391 https://orcid.org/0000-0002-7985-9103
Kurnia, Anang
Department of Statistics, IPB University
Sadik, Kusman
Department of Statistics, IPB University
Proportional Odds Model (POM); Non-Proportional Odds Model (NPOM); birth size; Likelihood Ratio test; Goodness of fit
The proportional odds model (POM) and the non-proportional odds model (NPOM) are very useful in ordinal modeling. However, the proportional odds assumption is often violated in practice. In this paper, the non-proportional odds model is chosen as an alternative model when the proportional odds assumption is not violated. This paper aims to compare Proportional Odds Model (POM) and Non-Proportional Odds Model (NPOM) in cases of birth size in Indonesia based on the 2017 Indonesian Demographic and Health Survey (IDHS) data. The results showed that in the POM there was a violation of the proportional odds assumption, so the alternative NPOM model was used. NPOM had better use than POM. The goodness of fit shows that the deviance test failed to reject H0, and the value of Mac Fadden R2 is higher than POM. The risk factors that have a significant influence on all categories of birth size are the residence and gender of the child.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/31331
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8291
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
PREDIKSI HARGA SAHAM MENGGUNAKAN SUPPORT VECTOR REGRESSION DENGAN ALGORITMA GRID SEARCH
Yasin, Hasbi
Prahutama, Alan
Utami, Tiani Wahyu
The stock market has become a popular investment channel in recent years because of the low return rates of other investment. The stock price prediction is in the interest of both private and institution investors. Accurate forecasting of stock prices is an appealing yet difficult activity in the business world. Therefore, stock prices forecasting is regarded as one of the most challenging topics in business. The forecasting techniques used in the literature can be classified into two categories: linear models and non linear models. One of forecasting techniques in nonlinear models is support vector regression (SVR). Basically, SVR adopts the structural risk minimization principle to estimate a function by minimizing an upper bound of the generalization. The optimal parameters of SVR can be use Grid Search Algorithm method. Concept of this method is using cross validation (CV). In this paper, the SVR model use linear kernel function. The accurate prediction of stock price, in telecommunication, is 92.47% for training data and 83.39% for testing data.
Keywords: Stock price, SVR, Grid Search, Linear kernel function.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8291
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/45503
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
EXTRA TREES METHOD FOR STOCK PRICE FORECASTING WITH ROLLING ORIGIN ACCURACY EVALUATION
Mahkya, Dani Al
Actuarial Science Study Program, Institut Teknologi Sumatera
Department of Statistics, IPB University
Notodiputro, Khairil Anwar
Department of Statistics, IPB University
Sartono, Bagus
Department of Statistics, IPB University
Randomized Trees; Extra Trees; Regression; Stock; Forecasting
Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/45503
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10082
2018-02-27T10:10:10Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
RANCANGAN D-OPTIMAL MODEL MICHAELIS MENTEN DAN EMAX DENGAN MATLAB
Widiharih, Tatik
Jurusan Statistika FSM Undip
Haryatmi, Sri
Jurusan Matematika FMIPA UGM
Gunardi, Gunardi
Jurusan Matematika FMIPA UGM
Wilandari, Yuciana
Jurusan Statistika FSM Undip
Michaelis Menten and Emax models are widely used in chemistry, pharmacokinetics and pharmacodynamics areas. D-optimal criteria is criteria with the purpuse to minimize the variance of the estimator of parameters in the model. In this paper will discuss the D-optimal design for Michaelis Menten and Emax models with homoscedastics error assumtion. Determination of D-optimal designs based on Generalied Equivalence Theorem Kiefer-Wolvowitz. We used minimally supported design with the proportion of each design point is uniform, lower bound of design region is design point and the others are interior points.
Keywords: D-optimal, Michaelis Menten, Emax, Minimally Supported Design, Homoscedastics
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10082
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/43905
2023-06-11T08:49:14Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
APPLICATION OF BIPLOT ANALYSIS WITH ROBUST SINGULAR VALUE DECOMPOSITION TO POVERTY DATA IN SULAWESI ISLAND
Taki, Febriyana
Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119
Yahya, Lailany
Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119
Payu, Muhammad Rezky Friesta
Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Science, Gorontalo State University, Jl. Prof Dr. Ing, B.J. Habibie, Bone Bolango Regency, Gorontalo, Indonesia, 96119
Poverty; Biplot Analysis; Robust Singular Value Decomposition; Outlier
Poverty is defined as an inability of the individual to meet basic needs for a decent life. According to BPS data in
2020, Sulawesi Island ranks fifth as the poorest island in Indonesia. This study aims to find out the mapping of areas and indicators of poverty in Sulawesi Island using Biplot Analysis with Robust Singular Value Decomposition approach for outlier research data. Based on the results of the study, there are five objects that are outlier and the information provided by the biplot amounted 98.45%. District/city that have similar characteristics are divided into 4 groups. The indicator of poverty that has the most diversity is the School Old Expectations Numbers (Var 4) and the one with the least diversity is Poor Households Using Clean Water (Var 8). Indicators of poverty that are positively correlated are Literacy Numbers (Var 1) and Non-Working Poor Population (Var 5), while the negative correlated are The Non-Working Poor Population (Var 5) and Poor Households Using Clean Water (Var 8). There are 19 districts/cities that have literacy values above the average of all districts/cities and 11 districts/cities that have a per capita expenditure value below the average of all districts/cities.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/43905
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13135
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
PEMODELAN REGRESI BERGANDA DAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH
Utami, Tiani Wahyu
Jurusan Statistika, Universitas Muhammadiyah Semarang
Rohman, Abdul
Jurusan Statistika, Universitas Muhammadiyah Semarang
Prahutama, Alan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X1), Inflation (X2), the GDP value (X3), UMR Value (X4), the percentage of GDP growth rate (X5), Hope of the old school (X6), the percentage of the labor force by age (X7) and the percentage of employment (X8). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region.
Keywords: multiple linear regression, geographiically weighted regression, open unemployement rate in Central Java.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13135
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/1155
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
PENENTUAN MODEL REGRESI TERPOTONG ATAS DENGAN METODE MAKSIMUM LIKEHOOD
Jalarno, Dydaestury
Ispriyanti, Dwi
Model regresi terpotong atas merupakan suatu model regresi dengan nilai-nilai variabel dependen y < a, dengan a adalah suatu titik potong atas yang dipilih berdasarkan penelitian. Dengan demikian, model regresi terpotong lebih tepat jika digunakan untuk penelitian yang berorientasi pada suatu karakteristik tertentu dari obyek pengamatan yaitu variabel dependennya. Distribusi yang digunakan untuk model regresi ini adalah distribusi normal terpotong atas. Estimasi parameter regresinya menggunakan metode Maksimum Likelihood dan metode iteratif Newton Raphson sedangkan pengujian signifikansi model menggunakan Uji Likelihood Rasio, uji t dan harga koefisien determinasi (R2)
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/1155
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18404
2018-04-04T11:28:11Z
media_statistika:FMT
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18404
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2500
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
KARAKTERISTIK UMUR PRODUK PADA MODEL WEIBULL
Sudarno, Sudarno
Long life of product can reflect its quality. Generally, good products have long life. There are functions that relationship with life as reliability function, hazard rate function, mean time to failure, and mean residual life. In this writing those functions be used to product which has the failure time of a component is distributed Weibull. The reliability function is exponential function. For value θ is constant, the reliability value is decrease function, if γ is greather with respect to time. Meanwhile hazard rate function could be monotone increase function, constant function, monotone decrease function, if doing by simulation with shape parameter by one. Really, the mean time to failure product hang on Weibull distribution parameters. But the mean residual life is reciprocal with respect to its reliability.
Keywords: Weibull Model, Reliability and Hazard Rate Functions, Mean Time to Failure, Mean Residual Life.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2500
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/16337
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
DETERMINAN IMPOR SERAT KAPAS DI INDONESIA TAHUN 1975-2014 (PENDEKATAN ERROR CORRECTION MECHANISM)
Hanifah, Nida'ul
Politeknik Statistika STIS
Kartiasih, Fitri
SEKOLAH TINGGI ILMU STATISTIK
The activity of textile sector and textile product (TPT) in Indonesia keeps growing from year to year.TPTIndustry has become the main contributor of foreign exchange from non-oil and gas sector. Unfortunately, the domestic supply of cotton fiber, main material of textile product, can’t fulfill textile industry’s demand. It forces the nation to import the raw materials. Based on the problem about the import that still exist until the present, it is necessary to do a research to analyze the development of cotton fiber import in Indonesia and to identify the factors affecting the development of Indonesian cotton fiber imports during 1975-2014. This research uses descriptive analysis and inference analysis. The descriptive analysis method used in this research is graphical analysis, while the inference analysis is Error Correction Mechanism (ECM) method. Based on the estimation made with ECM, it was found that 5 variables significantly affect the cotton import volume in the long term, including: real per capita Gross Domectic Product (GDP), international cotton fiber prices, domestic cotton fiber production, the demand of cotton fiber by domestic yarn spinning industry and textile product exports volume. While in short term, only 4 variables significantly affect thecotton fiber import volume: domestic cotton fiber production,the demand of cotton fiber by domestic yarn spinning industry, real per capita GDP and textile product exports volume.
Keywords: import, cotton fiber, Textile Industry and Textile Product (TPT),Error Correction Mechanism (ECM).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/16337
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2521
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
KOMPONEN UTAMA UNTUK PENGENDALIAN KUALITAS SECARA STATISTIK
Nurhasanah, Nunik
Safitri, Diah
Statistically Quality Control is a problem solving technique that used to check, control, analyze, bring off and repair product with statistical methods. One of the method that used statistically control quality is Principal Component Analysis. Principal Component Analysis is a multivariate technique that used to reduce the dimension of data. Principal component is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Statistically quality control with principal components is used by constructing multivariate control charts which consist of Ellipse Chart for first two principal components and Chart for unexplained principal components in Ellipse Chart.
Keywords : Principal Component Analysis, Ellipse Chart, Chart
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2521
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/23900
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
PERAMALAN DATA PENUMPANG KERETA API JANUARI 2013-NOVEMBER 2018 DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN)
Andriyani, Mira
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Subanar, Subanar
Departemen Matematika, FMIPA, Universitas Gadjah Mada
MODWT; RNN; Train Passengers
The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of passengers at the station so that it sometimes causes a accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result thanS ARIMA and RNN.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/23900
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4522
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
ESTIMASI PARAMETER DISTRIBUSI WEIBULL DUA PARAMETER MENGGUNAKAN METODE BAYES
Hazhiah, Indria Tsani
Sugito, Sugito
Rahmawati, Rita
Interval estimation of a parameter is one part of statistical inference. One of the methods that used is the Bayes method. A Bayesian method is combine prior distribution and distribution of samples, so that the posterior distribution can be obtained. Interval estimation using a method Bayes called credibel interval estimation. In this thesis, the distribution of the sample is used a two-parameter Weibull distribution scale-shape-version of survival distribution (reliability). Data that used are data that is not censored data type and data type II censored if prior distribution using non-informative which of the produce distribution the resulting posterior distribution is gamma distribution. Parameters of the sample distribution that to find out is a parameter that by the parameter c (shape parameter) known while the parameter b (scale parameter) had unknown.
Keywords: Bayes Method, Two-Parameters Weibull Distribution , Gamma Distribution, The Estimated Credible Interval.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4522
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/4830
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
IDENTIFIKASI POLA DISTRIBUSI CURAH HUJAN MAKSIMUM DAN PENDUGAAN PARAMETERNYA MENGGUNAKAN METODE BAYESIAN MARKOV CHAIN MONTE CARLO
Mukid, Moch. Abdul
Wilandari, Yuciana
especially for the management of regional water resources. In this study, we not only identify the distribution of maximum rainfall, but also estimate the parameter of its distribution. The research was conducted in the Grobogan District. Maximum rainfall in the district of Grobogan from 2006 to July 2012 was very varied, but over the years have a pattern unlikely to change. Highest maximum rainfall ranged in December, January, February and March while the lowest rainfall maskimum normally be in June, July and August. By using the Kolmogorov-Smirnov test on the significance level of 5% is known that the maximum rainfall from 2006 to 2012 in the District Grobogan follow a normal distribution with a value of D statistics is 0.089. This statistic produces a significance value of 0.518. By using the Bayesian Markov Chain Monte Carlo obtained the value for the parameter mean of normal distribution is 46.269 mm with a standard error reach into 4.005 mm.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4830
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/26635
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL
Nugroho, Didit Budi
Department of Mathematics and Data Science, Universitas Kristen Satya Wacana
Study Center for Multidisciplinary Applied Research and Technology (SeMARTy) https://fsm.uksw.edu/matematika/index.php/dbn http://orcid.org/0000-0002-3693-0950
Priyono, Agus
Department of Mathematics and Data Science, Universitas Kristen Satya Wacana
Susanto, Bambang
Department of Mathematics and Data Science, Universitas Kristen Satya Wacana
Skew Distribution; GARCH; Excel’s Solver; Volatility
The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/26635
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7644
2016-03-15T17:19:31Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
ANALISIS DATA INFLASI DI INDONESIA PASCA KENAIKAN TDL DAN BBM TAHUN 2013 MENGGUNAKAN MODEL REGRESI KERNEL
Suparti, Suparti
The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This study aims to analyze inflation in Indonesia after the goverment raised the price of electricity basic and fuel price in 2013 using kernel regression models. This method was good for data modeling inflation in Indonesia before. The goodness of a kernel regression model is determined by the chosen kernel function and wide bandwidth used. However, the most dominant is the selection of the wide bandwidth. In this study, determination of the optimal bandwidth by minimizing the Generalized Cross Validation (GCV).
By model the annual inflation data (Indonesia) December 2006 - December 2011, the inflation target in 2012 is (4,5 + 1 )% can be achieved both exactly and predictly, while the inflation target in 2013 is (4,5 + 1 )% cannot be achieved neither exactly nor predictly. The inflation target in 2013 can’t be achieve because since the beginning of 2013, there was a government policy to raise the price of electricity and the middle of 2013, there was an increase in fuel prices. The prediction of Indonesia inflation in 2014 by Gauss kernel is 6,18%.
Keywords: Inflation, Kernel Regression Models, Generalized Cross Validation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7644
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/35588
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
A STUDY OF GENERALIZED LINEAR MIXED MODEL FOR COUNT DATA USING HIERARCHICAL BAYES METHOD
Sunandi, Etis
Department of Mathematics, Bengkulu University
Department of Statistics, IPB University
Notodiputro, Khairil Anwar
Department of Statistics, IPB University
Sartono, Bagus
Department of Statistics, IPB University
Absolute bias; GLMM; illiteracy; MCMC; Poisson Log-Normal
Poisson Log-Normal Model is one of the hierarchical mixed models that can be used for count data. Several estimation methods can be used to estimate the model parameters. The first objective of this study was to examine the performance of the parameter estimator and model built using the Hierarchical Bayes method via Markov Chain Monte Carlo (MCMC) with simulation. The second objective was applied the Poisson Log-Normal model to the West Java illiteracy Cases data which is sourced from the Susenas data on March 2019. In 2019, the incidence of illiteracy is a very rare occurrence in West Java Province. So that, it is suitable as an application case in this study. The simulation results showed that the Hierarchical Bayes parameter estimator through MCMC has the smallest Root Mean Squared Error of Prediction (RMSEP) value and the absolute bias is relatively mostly similar when compared to the Maximum Likelihood (ML) and Penalized Quasi-Likelihood (PQL) methods. Meanwhile, the empirical results showed that the fixed variable is the number of respondents who have a maximum education of elementary school have the greatest risk of illiteracy. Also, the diversity of census blocks significantly affects illiteracy cases in West Java 2019.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/35588
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9200
2018-02-27T10:47:32Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
ANALISIS SPASIAL PENGARUH TINGKAT PENGANGGURAN TERHADAP KEMISKINAN DI INDONESIA (Studi Kasus Provinsi Jawa Tengah)
Rahmawati, Rita
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Safitri, Diah
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Fairuzdhiya, Octafinnanda Ummu
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Poverty is still being one of big problems in Indonesia. Any efforts are done to find a solution for this problem. Poverty itself can be caused of the high unemployment that occurs. With a number of unemployment, it will be lower income thus reducing also purchasing power and the ability to meet the needs of life thus causing poverty. This study analyzed the impact of unemployment to the poverty as involving spatial factors, using spatial regression analysis. Used data on poverty and unemployment in each regency in the central java, the analysis shows that based on likelihood ratio test, obtained LR test value 6,038 or p-value 0,014001 which means there is a spatial correlation. By testing model simultaneously nor individually using Breusch-Pagan test and Wald test, it show that both are significant, with BP = 6,7094; df = 1; p-value = 0,009591 and Wald statistic = 7,0238; p-value = 0,0080434. The results means there are spatial element in the relations between unemployment and poverty in central java so that SEM is more proper used than ordinary linear regression.
Keywords: Spatial Error Model (SEM), Spatial Autocorrelation, Spatial Heterogeneity
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9200
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/54942
2024-02-26T03:51:54Z
media_statistika:ART
nmb a2200000Iu 4500
"231222 2023 eng "
2477-0647
1979-3693
dc
COMPARISON OF LOGISTIC MODEL TREE AND RANDOM FOREST ON CLASSIFICATION FOR POVERTY IN INDONESIA
Sukarna, Sukarna
Universitas Negeri Makassar
IPB University
Notodiputro, Khairil Anwar
IPB University
Sartono, Bagus
IPB University
Decision Tree; Logistic Model Tree; Random Forest; Poverty; Model Classification
Classification methods are commonly employed to ensure homogeneous data within each group, facilitating the prediction of specific categories. The most frequently used classification models are Logistic Model Tree (LMT) and Random Forest (RF). This study aims to assess the accuracy rate in predicting the poverty status of regencies or towns across Indonesia, utilizing eight independent variables. The entire dataset was obtained from the official Central Bureau of Statistics website. The study investigates the accuracy of various iterations and combinations of training data. The results indicate that RF outperforms LMT in terms of accuracy, achieving a 100% improvement in iterations k=10 and k=500 and a 75% improvement in iteration k=100. Consequently, the RF proves to be more effective than the LMT for analyzing Indonesian poverty data, especially when incorporating all eight independent variables.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-12-22 11:43:06
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/54942
MEDIA STATISTIKA; Vol 16, No 2 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/50719
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
IMPLEMENTATION OF STOCHASTIC MODEL FOR RISK ASSESSMENT ON INDONESIAN STOCK EXCHANGE
Maruddani, Di Asih I
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro https://orcid.org/0000-0001-5778-0982
Trimono, Trimono
Data Science Study Program, UPN “Veteran” East Java, Indonesia
Mas'ad, Mas'ad
School of Sociology and Social Policy, University Park, The University of Nottingham, Nottingham, United Kingdom
Center for Data and Information Technology, Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, Indonesia
Stock Investment, Risk, Stochastic Model, Adjusted Expected Shortfall
Currently, financial assets become an alternative choice for investors in Indonesia to get maximum profits. The Indonesia Stock Exchange is the official capital market in Indonesia which is a place for trading financial assets. Stocks are listed as the most preferred financial asset by investors. In reality, stock investment is not a risk-free investment. The main risk that investors should face is the loss risk. This kind of risk can occur at any time. From that problem, this study aims to do risk assessment on the Indonesian stock market. The evaluation will be started with stock price index prediction using the Stochastic model (Geometric Brownian Motion Model and Jump Diffusion). Then, the result from that processes will be used to get loss risk prediction through the Adjusted Expected Shortfall model. By using the historical price of JKSE index from 01/08/21 to 31/08/22, Jump Diffusion is the best model to predict the JKSE index with MAPE value is 1.08%. Then, at the 95% confidence level and 1-day holding period, the expected loss risk using Adjusted Expected Shortfall model on 09/01/2022 is -0.02978.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/50719
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13125
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
APLIKASI REGRESI PARTIAL LEAST SQUARE UNTUK ANALISIS HUBUNGAN FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI KOTA YOGYAKARTA
Masruroh, Marwah
Fakultas MIPA, Universitas Negeri Yogyakarta
Subekti, Retno
Fakultas MIPA, Universitas Negeri Yogyakarta
Human Development Index is one of the indicators to measure the success of a region in the field of human development sector. There are several factors that affect Human Development Index, such as life expentancy, the literacy rate, the average length of the school, and the index of purchasing power. The aim in this paper is to analyze the relationship between factors that affect Human Development Index in Yogyakarta using regression analysis. One of the assumptions of classical regression is not going multicollinierity. Multicollinierity cause misinterpretation of regression coefficients with Ordinary Least Square (OLS) method. One method used to overcome multicollinierity is Partial Least Square (PLS). The result of Human Development Index data analysis showed there was a high correlation between the predictor variables or in other words going multicollinierity, so using PLS method, we obtained adjusted R2 of 99.3% Human Development Index variables can be explained by the four predictor variables. By using PLS method, multicollinierity resolved in the problem of violation in the linear regression assumption.
Keywords: IPM, OLS, regression, PLS.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13125
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18274
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
ANALISIS REGRESI SPASIAL DAN POLA PENYEBARAN PADA KASUS DEMAM BERDARAH DENGUE (DBD) DI PROVINSI JAWA TENGAH
Fatati, Inna Firindra
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Wijayanto, Hari
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Sholeh, Agus M.
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Dengue Hemorrhagic Fever (DHF) is one of the diseases that threaten human health. The cases of dengue fever in the district / city certainly has different characteristics, geographic condition, the potential of the region, health facilities, as well as other matters that lie behind them. Based on local moran index values are visualized through thematic maps, some area adjacent quadrant tends to be in the same group. There are two significant quadrant in describing the pattern of spread of dengue cases namely quadrant high-high and lowlow. This indicates a spatial effect on the number of dengue cases, so that the spatial regression analysis. Based on the value of and AIC, autoregressive spatial models (SAR) is good enough to be used in modeling the number of dengue cases in the province of Central Java. Factors that influence the number of dengue cases Central Java province in 2015 is the number of health centers per 1000 population, the number of polindes per 1000 population, population density (X3), percentage of people with access to drinking water sustainable decent (X6), the percentage of water quality net free of bacteria, fungi and chemicals (X7), and the number of facilities protected springs (X8).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18274
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2483
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
PENERAPAN GRAFIK PENGENDALIAN DEMERIT TERHADAP DATA KUALITATATIF
Rusgiyono, Agus
A product is represented as inappropriate considered into minor category up to critical, which than given by weight at characteristic of the inappropriate as a according to its importance level. Ploting all amount of inappropriate at one controller graph regardless of type will mislead. To solve it used by graph controller of demerit. Analysis step represent one of the operational step in program operation of quality with a purpose to understand stability and capability of proces wich underway.
Expectation of phase analyse can identify the root problem that caused incidence of variation of quality so that can be continued to repair phase. To understand the stability and capability of process which underway can be depicted with controller graph and analysis its. At this article will be studied demerit graph controller and analysis of capability at data qualitative.
Keyword : Stability, Capability and Weighted Graph Controller
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2483
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/16980
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
MODEL REGRESI POISON BIVARIAT DENGAN KOVARIAN KONSTAN
Kurniawan, Untung
Badan Pusat Statistik Kabupaten Klaten
Bivariate Poisson models are appropriate for modeling paired count data exhibiting correlation. This study aims to estimates the parameters and test hypothesis of bivariate Poisson regression on modeling the number of infant mortality and maternal mortality in Central Java 2015. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Results show that the percentage of births by health personnel, the percentage of pregnant women administered the K4 program, the percentage of pregnant women receiving Fe3 tablets, percentage of exclusively breastfed infants, and percentage of households behaved in a clean and healthy life are significant for the number of infant mortality in Central Java. The variables that have significant effect on maternal mortality are percentage of births by health personnel, percentage of maternal women receiving postpartum health services, and percentage of pregnant women receiving Fe3 tablets.
Keywords: Bivariate Poisson Regression, Infant Mortality, Maternal Mortality, Maximum Likelihood Estimation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/16980
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16980/41572
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16980/41574
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16980/41575
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/16980/41592
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2511
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
PEMERIKSAAN DATA BERPENGARUH DALAM MODEL REGRESI GAMMA
Hajarisman, Nusar
In certain cases, often encountered a group of data where the observed respone variable shaped non-negative and not symmetrical (skewed to the right). The case data such as this one can be found in the field of insurance, such as variable stating the amount of claims or states of time between when a customer claims to obtain these claims. To handle such cases one of them is by using a generalized linear model, with respone variables that follow the gamma distribution. The paper discusses the inspection data outliers and influential data in the modeling of the respone following the gamma distribution. Several statistical measures used to examine the outlier data is the value of leverage, standardized deviance residual, standardized Pearson residual, and residual likelihood. Then the data outliers potentially influential data will be examined using Cook's distance statistics.
Keywords: Gamma Distributions, Leverage, Standardized Deviance Residual, Standardized Pearson Residual, And Residual Likelihood, Cook's Distance Statistics
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2511
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/23406
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
EXPECTED SHORTFALL DENGAN SIMULASI MONTE-CARLO UNTUK MENGUKUR RISIKO KERUGIAN PETANI JAGUNG
Rahmawati, Rita
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Rusgiyono, Agus
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Hoyyi, Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Maruddani, Di Asih I
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
In risk management, risk measurement plays an important role in allocating capital as well as in controlling (and avoiding) worse risk. Estimating the risk value can be done by using a risk measure. The most popular method for evaluating risk is Value at Risk (VaR). But VaR does not fulfill the coherency as a measure of risk effectiveness. In this paper, we propose Expected Shortfall (ES) which has coherency nature. ES is defined as the conditional expectation of losses beyond VaR of the same confidence level over the same holding period. For measuring ES, we use Monte-Carlo Simulation Method. This method is applied for measuring risk that will be faced by corn’s farmers due to the changes in corn prices in Pemalang city. The results show that the ES value is 0.085472 at 95% confidence level and one-month holding period. This number means that a farmer will face 8.5472% of investment as maximum loss exceeding of VaR.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/23406
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2630
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
UJI STASIONERITAS DATA INFLASI DENGAN PHILLIPS-PERON TEST
Maruddani, Di Asih I
Tarno, Tarno
Anisah, Rokhma Al
The classical regression model was devised to handle relationships between stationary variables. It should not be applied to nonstationary series. A time series is therefore said to be stationary is its mean, variance, and covariances remain constant over time. A problem associated with nonstationary variables, and frequently faced by econometricians when dealing with time series data, is the spurious regression. An apparent indicator of such spurious regression was a particularly low level for the Durbin-Watson statistics, combined with an acceptable R2. Statistical test for stationarity have proposed by Dickey and Fuller (1979). The distribution theory supporting the Dickey-Fuller test assumes that the errors are statistically independent and have a constant variance. Phillips and Peron (1988) developed a generalization of the Dickey-Fuller procedure that the error terms are correlated and not have constant variance. In this paper, we use Phillips-Peron test for inflation data in Indonesia for the time period 1996-2003. The data showed upward trend and the error terms are correlated. The empirical results showed that the inflation data in Indonesia is a nonstationary series.
Keywords : stationarity, non autocorrelation, Phillips-Peron Test, inflation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2630
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22744
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
PERAMALAN LANGSUNG DAN TIDAK LANGSUNG MARKET SHARE MOBIL MENGGUNAKAN ARIMAX DENGAN EFEK VARIASI KALENDER
Titi, Dea Astri
Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)
Kuswanto, Heri
Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)
Suhartono, Suhartono
Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)
ARIMAX; Daihatsu; Direct; Indirect; Market share
Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22744
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/22744/91191
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/36771
2022-07-27T00:34:36Z
media_statistika:FMT
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
Front Matter Vol. 13 No. 1 2020
Statistika, Media
Cover dan Daftar Isi Media Statistika Vol. 13 No. 1 Juni 2020
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36771
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5668
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
PROSES ANTRIAN DENGAN KEDATANGAN BERDISTRIBUSI POISSON DAN POLA PELAYANAN BERDISTRIBUSI GENERAL
Sugito, Sugito
Hoyyi, Abdul
In the queuing process, the distribution testing is performed to obtain the distribution of arrival and service distributions. Customer arrival distribution is obtained based on the number of arrivals or inter-arrival time. Service distribution is obtained based on the number of arrivals or inter-arrival time. In this paper we will discuss the process in queuing with the arrival of the Poisson distribution and the general pattern of service distribution
Keywords : Queuing, Arrival Distribution, Service Distribution
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5668
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/35882
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING
Haryati, Anisa Eka
Magister Pendidikan Matematika, Universitas Ahmad Dahlan
Surono, Sugiyarto
Department Mathematic, Universitas Ahmad Dahlan http://uad.ac.id http://orcid.org/0000-0001-6210-7258
Fuzzy Subtractive Clustering; Hamming; Combination of Minkowski Chebysev
Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/35882
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8492
2018-02-27T10:58:42Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
ANALISIS KLASIFIKASI KABUPATEN DI JAWA TENGAH BERDASARKAN POPULASI TERNAK MENGGUNAKAN FUZZY CLUSTER MEANS
Wilandari, Yuciana
Jurusan Statistika, FSM, Universitas Diponegoro
Mukid, Moch. Abdul
Jurusan Statistika, FSM, Universitas Diponegoro
Megawati, Nurhikmah
Jurusan Statistika, FSM, Universitas Diponegoro
Sutarno, Yulia Agnis
Jurusan Statistika, FSM, Universitas Diponegoro
One of the fundamental problems that always exist in a regions in Indonesia is the problem of poverty. Various poverty reduction efforts initiated by the Central Government and the Regions is now experiencing growth and significant shifts in accordance with the direction and context of poverty reduction targets. To overcome poverty, one of the things done by the Central Java provincial government is to help livestock. Livestock types cultivated in Central Java, is a large livestock, namely cattle (beef / dairy), buffalo and horses, while small livestock consists of goats, sheep and pigs. For that conducted the study to classify cities in Central Java into groups based on livestock population. The grouping using fuzzy cluster analysis means. From this study showed that of the three kinds of clusters obtained many tried to do the most accurate cluster is 3 clusters with Xie-Beni index 0,3279177, with cluster 1 are 20 city, cluster 2 are 12 City and cluster 3 there are 3 City.
Keywords: Classification, Fuzzy Cluster Means, Livestock
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8492
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/56632
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231107 2023 eng "
2477-0647
1979-3693
dc
MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY
Hakim, Arief Rachman
Department of Statistics, Diponegoro University
Santoso, Rukun
Department of Statistics, Diponegoro University
Yasin, Hasbi
Department of Statistics, Diponegoro University https://orcid.org/0000-0002-4887-9646
Rochayani, Masithoh Yessi
Department of Statistics, Diponegoro University
Rainfall; Geometric Gaussian; Max-stable Process; Spatial.
Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/56632
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/56632/181544
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/42304
2022-07-28T02:52:59Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
STRUCTURAL EQUATION MODELING FOR ANALYZING THE TECHNOLOGY ACCEPTANCE MODEL OF STUDENTS IN ONLINE TEACHING DURING THE COVID-19 PANDEMIC
Annas, Suwardi
Statistics Study Program, Universitas Negeri Makassar
Ruliana, Ruliana
Statistics Study Program, Universitas Negeri Makassar
Sanusi, Wahidah
Mathematics Department, Universitas Negeri Makassar
Learning Management System; SYAM-OK; Technology Acceptance Model; and Structural Equation Modeling
Online teaching can be a solution in the learning process during the pandemic to stop the spreading of the Covid-19 infection. Universitas Negeri Makassar (UNM) as an educational institution provided a Learning Management System (LMS) to support the online teaching and learning process with the platform name SYAM-OK. In this research, we examine the behavioral model of a student's acceptance of the use of an information system SYAM-OK in online teaching. 120 students in the sample used online teaching fully during the pandemic. The data was obtained from an online questionnaire using a google form whose contents were based on Technology Acceptance Model (TAM). The variable of TAM consists of Perceived Ease of Use, Perceived Usefulness, Attitude Towards, Behavioral Intention, and Actual Use. The Structural Equation Modeling (SEM) PLS method was used in this research for modeling the relationship between TAM variables. Based on the results of the SEM we obtained that Perceived Usefulness significantly affects the Attitude Towards and Attitude Towards significantly affects the behavioral intention. By using the bootstrapping and T statistics, we conclude that SEM has identified the significant effects between variables of TAM.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/42304
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/42304/130281
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11720
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
ANALISIS KLASIFIKASI MASA STUDI MAHASISWA PRODI STATISTIKA UNDIP dengan METODE SUPPORT VECTOR MACHINE (SVM) dan ID3 (ITERATIVE DICHOTOMISER 3)
Ispriyanti, Dwi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Hoyyi, Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Graduation is the final stage of learning process activities in college. Undergraduate study period in UNDIP’s academic regulations is scheduled in 8 semesters (4 years) or less and maximum of 14 semesters (7 years). Department of Statistics is one of six departments in the Faculty of Science and Mathematics UNDIP. Study period in this department can be influenced by many factors. Those factor are Grade Point Average (GPA) or IPK, gender, scholarship, parttime, organizations, and university entrance pathways. The aim of this paper is to determine the accuracy factors classification. We use SVM (Support Vector Machine method) and ID3 (Iterative Dichotomiser 3). The comparison of SVM and ID3 method, both for training and testing the data generate good accuracy, namely 90%. Especially ID3 training data gives better result than SVM.
Keywords: SVM, ID3
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11720
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/15602
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang)
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro http://orcid.org/0000-0002-4887-9646
Ispriyansti, Dwi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.
Keywords:
Birthweight, Classification, LBW, WPNN.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15602
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/1160
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
INFERENSI DATA UJI HIDUP TERSENSOR TIPE II BERDISTRIBUSI RAYLEIGH
Widiharih, Tatik
Program Studi Statistika Jurusan Matematika
FMIPA Universitas Diponegoro
Jl. Prof. H. Soedarto, SH, Tembalang, Semarang 50255
Mardjiyati, Wiwin
Program Studi Statistika Jurusan Matematika
FMIPA Universitas Diponegoro
Jl. Prof. H. Soedarto, SH, Tembalang, Semarang 50255
Abstrak
Analisis data uji tahan hidup merupakan salah satu teknik analisis statistika yang banyak digunakan di bidang industri dan kesehatan. Data waktu hidup dapat berupa data lengkap atau data tersensor, dan merupakan variabel random nonnegatif. Estimator titik untuk parameter q digunakan MLE, kemudian MLE tersebut digunakan untuk uji kecocokan distribusi Rayleig dengan metode Anderson Darling. Estimator titik uji tahan hidup meliputi rata-rata waktu kegagalan (mean time to failure / MTTF), fungsi kegagalan h(t), dan fungsi ketahanan S(t). Estimasi interval dilakukan dengan metode besaran pivot.
Kata kunci: data tersensor, MLE, rata-rata waktu kegagalan, fungsi kegagalan, fungsi ketahanan
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/1160
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/19739
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION
Yasin, Hasbi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro http://orcid.org/0000-0002-4887-9646
Warsito, Budi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Hakim, Arief Rachman
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.
Keywords: Banten, Economic growth, MGWR.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/19739
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/19739/51213
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2506
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
ANALISIS KUALITAS PELAYANAN DAN PENGENDALIAN KUALITAS JASA BERDASARKAN PERSEPSI PENGUNJUNG
Sudarno, Sudarno
Rusgiyono, Agus
Hoyi, Abdul
Listifadah, Listifadah
One of the factors which determine customer safisfaction is costumer perceive about service quality that focus to five service quality dimension that are tangible, reliability, responsiveness, assurance, and empathy. This research study service serve quality at UPT Perpustakaan Universitas Diponegoro Semarang with object to know customer perceive with respect to some variables in service quality dimension and satisfaction level. Importance-Performance Analysis used to map relation between importance with performance of respective variables to be and see gap between performance with importance of them variables. Customer Satisfaction Index (CSI) used to analyze all satisfaction respondent level. The T2 Hotelling control chart to know servicing process stability with respect to costumer perceive. Research result shows that the gap is all negative value. It means library performance that represented by 21 variables include 5 service quality dimension still under expected costumer. The value CSI is 62,903% that meaning at enough satisfaction criterion. There are five points at above upper control limit in the T2 Hotelling control chart. Therefore it can be said that process haven’t been controlled by statistical.
Keywords: Service Quality, Importance-Performance Analysis, Customer Satisfaction Index, Hotelling T2 Control Chart.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2506
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18938
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
ANALISIS DAMPAK GUNCANGAN HARGA MINYAK MENTAH TERHADAP MAKROEKONOMI INDONESIA: APLIKASI VECTOR ERROR CORRECTION MECHANISM
Andre, Michael
Badan Pusat Statistik
Nasrudin, Nasrudin
Sekolah Tinggi Ilmu Statistik
Indonesian Crude Oil Price (ICP) often fluctuates by the shock of world oil prices. Because of its important role, the fluctuations or shocks in ICP will affect Indonesia's macro economy. To overcome this problem, this study analyzes the impact of the crude oil price shocks on Indonesia's macro economy which includes economic growth and the money supply (M2) during 2010-2016 using Vector Error Correction Mechanism (VECM). The results show that short-term fluctuations of ICP have a significant and positive effect on economic growth but have a non-significant effect on the money supply. In the long term equilibrium, ICP have a positive and significant effect to both economic growth and money supply which in line with Impulse Response Function (IRF) and Decomposition of Variance (FEDV) analysis. Given its positive impact, the recent decline in oil prices will harm the Indonesian economy. Therefore, the government needs to reduce its dependence on crude oil exports and accurately predict the crude oil price in the future.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18938
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2607
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
GRAFIK PENGENDALI NON PARAMETRIK EMPIRIK
Santoso, Rukun
Shewhart control chart is constructed base on the normality assumption of process. If the normality is fail then the empirical control chart can be an alternative solution. This means that the control chart is constructed base on empirical density estimator. In this paper the density function is estimated by kernel method. The optimal bandwidth is selected by leave one out Cross Validation method. The result of empirical control chart will be compared to ordinary Shewhart chart.
Key words : Control chart, Kernel, Cross Validation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2607
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20959
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
MODEL DEBIT DAERAH ALIRAN SUNGAI JANGKOK BERDASARKAN HASIL PREDIKSI MODEL STATISTICAL DOWNSCALING NONPARAMETRIK KERNEL CURAH HUJAN DAN TEMPERATUR
Hadijati, Mustika
Program Studi Matematika, FMIPA, Universitas Mataram
Irwansyah, Irwansyah
Program Studi Matematika, FMIPA, Universitas Mataram
Statistical Downscaling; GCM; CART; Kernel Nonparametric
River water discharge is influenced by climatic conditions. River water discharge is important information for water resources management planning, so it is necessary to develop river water discharge model as basis of its predictions. In order to get the result of predictions of river water discharge with high accuracy, it is developed a model of river water discharge based on the predictions of local climate (local rainfall and temperature) that are influenced by global climate conditions..Prediction of local climate is based on the Kernel nonparametric statistical downscaling model by utilizing GCM data. GCM data is a high dimensional global data, so data pre-processing is needed to reduce data dimension. It is done by CART algoritm. Statistical downscaling model is used to predict local rainfall and temperature. The prediction results are quite good with relatively small RMSE value. They are used to develop model of river water discharge. Modeling river water discharge is carried out using the Kernel nonparametric approach. The model of river water discharge produced is quite good because it can be used to predict river water discharge with relatively small RMSE.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20959
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/28223
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS
Rifa, Isna Hidayatur
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret
Pratiwi, Hasih
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret
Respatiwulan, Respatiwulan
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret
Earthquake, Data Mining, Clustering; K-Medoids Algorithm; K-Means Algorithm
Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/28223
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/30781
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
SUSCEPTIBLE INFECTED RECOVERED (SIR) MODEL FOR ESTIMATING COVID-19 REPRODUCTION NUMBER IN EAST KALIMANTAN AND SAMARINDA
Sifriyani, Sifriyani
Study Program of Statistics, Department of Mathematics, Mulawarman University https://orcid.org/0000-0002-4616-775X
Rosadi, Dedi
Department of Mathematics, Gadjah Mada University https://orcid.org/0000-0003-2689-253X
COVID-19; Estimate; SIR; Simulation; Reproduction
Modeling and analysis of Covid-19 data, especially on the modeling the spread and the prediction of the total number of cases for Indonesian data, has been conducted by several researchers. However, to the best of our knowledge, it has not been studied specifically for East Kalimantan Province data. The study of the data on the level of provincial and District/City level could help the government in making policies. In this study, we estimate the Covid-19 reproduction number, calculate the rate of recovery, the rate of infection, and the rate of death of East Kalimantan Province and Samarinda City. We also provide a prediction of the peak of the infection cases and forecast the total incidence of Covid-19 cases until the end of 2020. The model used in this research is the Susceptible Infected Recovered (SIR) model and the data used in the study was obtained from the East Kalimantan Public Health Office.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/30781
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5663
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
PEMODELAN DATA KEMISKINAN DI PROVINSI SUMATERA BARAT DENGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)
Maggri, Ilham
Ispriyanti, Dwi
Counting the number of poor have often been modeled as a function of a global regression, which meant that the regression coefficient value applied to all geographic regions. Though this assumption was not always valid because of the differences in geographic locations most likely causing the spatial heterogeneity. In case of spatial heterogeneity, the regression parameters would vary spatially, so if the global regression model was applied, would produce an average value of those regression parameters which vary spatially. This study uses the method Geographically Weighted Regression (GWR) to analyze data that contains spatial heterogeneity. In GWR model estimation, the model parameters are obtained by using the Weighted Least Square (WLS) which gives a different weighting in each location. This study discusses the factors that influence the level of poverty in the province of West Sumatra. Suitability test of the model results shows that there is no influence of spatial factors on the level of poverty in the province of West Sumatra. The results shows that there are four variables that are assumed to affect the level of poverty in the province of West Sumatra, they are the variable of floor space, the facility to defecate, ability to pay the cost of health center / clinic and education levels of household head. The four variables have a similar effect in every city and county.
Keywords : Poverty, Spatial Heterogeneity, Geographically Weighted Regression
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5663
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/27338
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)
Saputro, Dewi Retno Sari
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret https://scholar.google.co.id/citations?user=ShRSuLYAAAAJ&hl=id http://orcid.org/0000-0002-6569-394X
Sulistyaningsih, Sulistyaningsih
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret
Widyaningsih, Purnami
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret
Additive noise; Ensemble, parameter estimation; SAR Model
The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/27338
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8292
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
ANALISIS MODEL PASIEN RAWAT JALAN RUMAH SAKIT KARIADI DENGAN PENDEKATAN POISSON-EKSPONENSIAL
Ispriyanti, Dwi
Sugito, Sugito
Rusgiyono, Agus
In daily activities, we often face in a situation of queuing. Most people have experiences in a queuing situation or a waiting situation . The queuing can be found easily in a human life. For example is the queuing in the Kariadi Hospital. The Queuing occur from the registration to the service stage. Similarly, in ambulatory patients of Kariadi Hospital, so it is necessary to analyze the queuing effectivity, whether the queueing system is optimal or not. One of the statistical methods to analyze the things mentioned above are queuing theory. This research is used to analyze the queuing service system at the Kariadi hospital
Keywords: Kariadi Hospital, The Queuing
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8292
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/53864
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"230427 2023 eng "
2477-0647
1979-3693
dc
CORPORATE FINANCIAL DISTRESS PREDICTION USING STATISTICAL EXTREME VALUE-BASED MODELING AND MACHINE LEARNING
Prastyo, Dedy Dwi
Department of Statistics, Institut Teknologi Sepuluh Nopember https://scholar.google.com/citations?user=AWNTSFoAAAAJ&hl=id https://orcid.org/0000-0003-1194-769X
Savera, Rizki Nanda
Department of Statistics, Institut Teknologi Sepuluh Nopember
Adiwibowo, Danny Hermawan
Bank Indonesia
Classification; feature selection; financial distress; GEVR; imbalanced data; machine learning.
The industrial sector plays a leading role in an economy such that the financial stability of companies from this sector be a big concern. Two financial ratios, i.e., the Interest Coverage Ratio (ICR) and the Return on Assets (ROA), are used to determine the corporate financial distress conditions. This work considers two schemes for determining financial distress. First, a company is categorized as distressed if either ICR<1 or ROA<0. The second scheme is for when both ICR<1 and ROA<0 are met. The proportion of distressed and non-distressed companies is imbalanced. Our work views the distressed companies (minority class) as a rare event, causing the proportion to be extremely small, such that the Extreme Value Theory can be employed. The so-called Generalized Extreme Value regression (GEVR), developed from GEV distribution, predicts the distressed labels. The GEVR's performance is compared using machine learning with and without feature selection. The feature selection in GEVR uses backward elimination. The model for prediction employs a drift or windowing concept, i.e., using past-period predictors to predict the current response. The empirical results found that the GEVR, with and without the feature selection, provides the best prediction for financial distress.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/53864
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/47502
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
THE INTERPLAY BETWEEN CLUSTERS, COVARIATES, AND SPATIAL PRIORS IN SPATIAL MODELLING OF COVID-19 IN SOUTH SULAWESI PROVINCE, INDONESIA
Aswi, Aswi
Statistics Study Program, Universitas Negeri Makassar http://orcid.org/0000-0002-0639-2936
Tiro, Muhammad Arif
Statistics Study Program, Universitas Negeri Makassar
Sudarmin, Sudarmin
Statistics Study Program, Universitas Negeri Makassar
Sukarna, Sukarna
Mathematics Department, Universitas Negeri Makassar
Cramb, Susanna
Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology
Bayesian CAR Localised; Clustering; Covid-19; Relative Risk
A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/47502
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10083
2018-02-27T10:09:56Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
MENENTUKAN MATRIKS PELUANG TRANSISI UNTUK WAKTU OKUPANSI MENGGUNAKAN TRANSFORMASI LAPLACE DAN MATRIKS EKSPONENSIAL
Sudarno, Sudarno
Jurusan Statistika FSM Undip
The transition probability matrix is a matrix which contains some probability among two state. It has properties that every probability is non-negative and sum by row at every state is one. This paper want to determine the transition probability matrix by Laplace transform and exponential of a matrix methods. To construct the transition probability matrix by Laplace transform depends on identity matrix and generator matrix, but by matrix exponential method depends on generator matrix only. In this research obtained result that matrix exponential method easier than Laplace transformation. Because it is aided by software and programming. The transition probability matrix can be used to predict probability each other state. It could be used to predict value of state probability on long-term or limiting behavior, too. Otherwise, the transition probability mtrix could be used to construct occupancy times matrix.
Keywords: Generator matrix, Laplace transform, Exponential matrix, Occupancy times matrix.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10083
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/15597
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Rancangan D-Optimal Model Gompertz dengan Maple
Widiharih, Tatik
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Warsito, Budi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Gompertz model is used in many areas including biological growth studies, animal and husbandry, chemistry, and agricultural. Locally D-optimal designs for Gompertz models with three parameters is investigated. We used the Generalized Equivalence Theorem of Kiefer and Wolvowitz to determine D-optimality criteria. Tchebysheff system is used to decide that the D-optimal design is minimally supported design or nonminimally supported design. The result, D-optimal design for Gompertz model is minimally supported design with uniform weight on its support.
Keywords:
D-optimal, Generalized Equivalence Theorem, Tchebysheff System, Minimally Supported, Uniform Weight.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15597
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2469
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
FUZZY PARAMETRIC SAMPLE SELECTION MODELS OF MARRIED WOMEN FOR NON-PARTICIPATION BY MLE : CASE STUDY THE MPFS-1994
Safiih, L. Muhamad
Triana, Yaya Sudarya
Models with sample-selection biases are widely used in various fields of economics such as labour economics (see Maddala, Amemiya, and Mroz). The models are usually estimated by Heckman's two-step estimator. However, Heckman's two-step estimator often performs poorly (see Wales and Woodland, Nelson, Paarsch, and Nawata). The data used in this study originated from the survey was conducted by the National Population and Family Development Board of Malaysia under the Ministry of Women, Family and Community Development of Malaysia, called the Malaysian Population and Family Survey 1994 (MPFS, 1994). The survey was conducted through a questionnaire, were randomly and specifically for married women. The data set focus on married women which provides information on wages, educational attainment, household composition and other socioeconomic characteristic. The Original sample data based on Mroz (1987), there are 4444 records married women. It is necessary to use the maximum likelihood method to estimate the models in such cases. For solving uncertainty data of a parametric sample selection model, in this paper needs to consider the models estimation using fuzzy modeling approach, called Fuzzy Parametric Sample Selection Model (FPSSM). Fuzzy Parametric sample selection model (FPSSM) is builds as a hybrid to the conventional parametric sample selection model. Finally, the result showed, FPSSM by Maximum Likelihood Estimator (MLE) estimates of the mean, Standard Deviation (SD).
Keywords: Econometrics, Fuzzy Number, Heckman Two-Step Estimator, Married Women, MLE, Non-participation, Sample Selection Model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2469
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18405
2018-04-04T11:28:45Z
media_statistika:FMT
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18405
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2501
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
ANALISIS SISTEM ANTRIAN KERETA API DI STASIUN BESAR CIREBON DAN STASIUN CIREBON PRUJAKAN
Sugito, Sugito
Fauzia, Marissa
Queue system is a group of customer, service, and some rules to regulate arrival customers. Queue happened if a customers which need a serve more than service capacity. Phenomenon queue will find easily in public facility. One of is train queue at Cirebon Main Train Station and Cirebon Prujakan Train Station. Queue happened from train awaiting to be ridden away and from train which would to go to station, so that makes sometimes inappropriate arrival and departure the train of schedule resulting cumulative of train passenger candidate. To analyse problems of train queue happened in station Cirebon can be applied the application of the queue theory. The steps must to do is by to create the examination where the queue happened. Based on those analysis can be known queue model and performance measure of queue system. And from data analysis can get two best kind of model for service system at Cirebon Main Train Station, that is (M/M/1):(GD/∞/∞) and (G/G/3):(GD/∞/∞). And two model service system at Cirebon Prujakan Train station, that is (M/G/2):(GD/∞/∞) and (M/G/1):(GD/∞/∞).
Keywords : Queue System, The Cirebon Station, Queue Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2501
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/21511
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION
Prahutama, Alan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro https://scholar.google.co.id/citations?user=HUaHwXUAAAAJ&hl=id
Warsito, Budi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Mukid, Moch. Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression.
Keywords: Maternal and Infant mortality, Poisson, GWPR
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/21511
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2522
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
PREDIKSI TERJANGKITNYA PENYAKIT JANTUNG DENGAN METODE LEARNING VECTOR QUANTIZATION
Hidayati, Nurul
Warsito, Budi
Learning Vector Quantization (LVQ) is a method that train the competitives layer with supervised. The competitives layer will learn automatically to classify the input vector given. If some input vectors has the short distance then the input vector will be grouped into the same class. The LVQ method can be used to classify the data into some classes or categories. At this paper, the LVQ method will be applied to classify if someone is suffer potenciate of heart desease or not. The data that be trained are 268 data of heart desease patient from UCI (University of California at Irvine) with 10 variables that are factors influence that infected of heart desease. From some trials showed that the learning rate (α) = 0.25, decrease of learning rate (Decα) = 0.1, and the minimum learning rate (Minα) = 0.001 are values that give a good prediction with level of accuracy is about 66.79 %.
Keywords: Learning Vector Quantization, Classify, Heart Desease
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2522
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/24804
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
Sartika, Qorina Rara
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Widiharih, Tatik
http://stat.undip.ac.id/?page_id=895
Mukid, Moch Abdul
Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst losses that occur in the stock portfolio with a certain level of confidence and in certain period of time. In general, financial data has a high volatility value, which is caused the variance of residual model is not constant and nonnormally distributed. In this case, Copula-GARCH can be used to calculate the VaR. The Generalized Autoregressive Conditional Heterocedasticity (GARCH) model can resolve the time series models that have non-constant residual variance. This research use the t-Copula to model the dependency structure in the combined distribution of stock returns. The t-copula function is good in terms of reaching the extreme value state that often occurs in the financial data of stock returns and has heavytails. The empirical data uses the stock return data of PT. Indofood Sukses Makmur, Tbk (INDF) and Bank Mandiri (Persero) Tbk (BMRI) in the period of October 8, 2012 - October 8, 2017. In this research, Value at Risk is calculated using the period 1 day ahead at 90% confidence level that is 0.042, at 95% confidence level that is 0.025 and at 99% confidence level that is 0.017 with weight of each stock is 50%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/24804
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4521
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
ANALISIS PENGARUH STRATEGI BAURAN PEMASARAN TERHADAP PEMILIHAN MEREK LAPTOP MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL (Studi Kasus Mahasiswa Universitas Diponegoro)
Himmah, Faiqotul
Wuryandari, Triastuti
Hoyyi, Abdul
One of necessity is considered very important in this era is necessity for information. The tools that support necessity of comsumer for information, such as computer that use battery or better known as laptop. Laptop is a product often used by businessman/enterprise and academic actors also the student are no exception. There are many laptop brands that revolve in Indonesia, are the Acer brand, Toshiba, Hp, Axioo, Dell, and the brand in addition to those brands. This research aim to know the effect of marketing mix strategy, which consist of three variable factors: product, price, and promotion to the selection of laptop brand in Diponegoro University students. The sample of research taken by using non probability sampling, that is purposive sampling technique dan accidental sampling technique. Analysis that used is multinomial logistic regression analysis, a regression analysis to solve problems where dependent variable has more than 2 categories with several independent variables. Based on the significance test for the overall model and the wald test for each parameter coefficient, consider that three of the marketing mix variables has a relationship with the selection of laptop brand. The biggest probability estimates for the Acer brand in the group with medium product, high price, and high promotion in the amount of 77.461%. The biggest probability estimates for the Toshiba brand in the group with highproduct, high price, and medium promotion in the amount of 49.239%. The biggest probability estimates for the Hp brand in the group with medium product, medium price, and medium promotion in the amount of 46.074%. The biggest probability estimates for the Axioo-Dell brand in the group with with medium product, medium price, and medium promotion in the amount of 14.764%. The biggest probability estimates for the other brands in the group with medium product, high price, and medium promotion in the amount of 22.134%.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4521
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/4831
2021-06-30T10:14:26Z
media_statistika:ART
nmb a2200000Iu 4500
"121217 2012 eng "
2477-0647
1979-3693
dc
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI INDEK MUTU BENANG MENGGUNAKAN METODE POHON REGRESI (Studi Kasus di PT. Industri Sandang Nusantara Unit Patal Grati)
Dewi, Hesti Sari
Wilandari, Yuciana
Sudarno, Sudarno
Quality for ripe material (yarn) really necessary for the company, therefore needs to control the product (ripe material), so we are able to know the unmatched product percentage of the company standard and to know the cause of the unmatched. The appropriate method to know the influential factor to company yarn quality successes, among those regression tree method. Regression tree is one of CART’s classification method. CART is a useful non parametric statistical method to get an accurate data group as distinguishing as of a classification. Because it has continuous type of response variable, so that CART can create regression tree. Regression tree is utilized to figure relationship among one response variable with one or more predictor variable that gets continued character and also category. The variables that have influence for yarn quality index at PT. Industri Sandang Nusantara Patal Grati’s Unit are raw material, machine output year, air humidity (RH) and hall temperature. The result of the research is that the year of machine output variable is the most influence to foot up yarn quality index and has main contribution in the formation of regression tree.
Keywords : Regression tree, CART, Yarn quality index, Rayon.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4831
MEDIA STATISTIKA; Vol 5, No 2 (2012): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/32568
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS
Gubu, La
Jurusan Matematika, FMIPA, Universitas Halu Oleo
Departemen Matematika, FMIPA, Universitas Gadjah Mada https://orcid.org/0000-0003-0726-4845
Rosadi, Dedi
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Abdurakhman, Abdurakhman
Departemen Matematika, FMIPA, Universitas Gadjah Mada
business sector; portfolio; Sharpe ratio; robust estimation; portfolio performance
In recent years there have been numerous studies on portfolio selection using cluster analysis in conjunction with Markowitz model which used mean vectors and covariance matrix that are estimated from a highly volatile data. This study presents a more robust way of portfolio selection where stocks are grouped into clusters based on business sector of stocks. A representative from each cluster is selected from each cluster using Sharpe ratio to construct a portfolio and then optimized using robust FCMD and S-estimation. Calculation Sharpe ratio showed that this method works efficiently on large number of data while also robust against outlier in comparison to k-mean clustering. Implementation of this method on stocks listed on the Indonesia Stock Exchange, which included in the LQ-45 indexed for the period of August 2017 to July 2018 showed that portfolio performance obtained using clustering base on business sector of stocks combine with robust FMCD estimation is outperformed the other possible combination of the methods.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/32568
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
ind
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7652
2016-03-15T17:19:31Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
MODEL PREDIKSI CURAH HUJAN DENGAN PENDEKATAN REGRESI PROSES GAUSSIAN (Studi Kasus di Kabupaten Grobogan)
Mukid, Moch. Abdul
Sugito, Sugito
Forecasting method of rainfall has developed rapidly, ranging from the deterministic approach to the stochastic one. Deterministic approach is done through an analysis based on physical laws expressed in mathematical form, which identify the relationships between rainfall and temperature, air pressure, humidity and the intensity of solar radiation. Similarly, there are some stochastic models for the prediction of rainfall that have been commonly used, for instances, the model Autoregressive Integrated Moving Average (ARIMA), Fourier analysis and Kalman filter analysis. Some researchers about climate and weather have also developed a predictive model of rainfall based on nonparametric models, especially models based on artificial neural networks. Above models are based on classical statistical approach where the estimation and inference of model parameters only pay attention to the information obtained from the sample and ignore the initial information (prior) of parameter model. In this research, prediction model with Gaussian process regression approach is used for predicting the monthly rainfall. Gaussian process regression uses a stochastic approach by assuming that the amount of rainfall is random. Based on the value of Root Mean Square Error Prediction (RMSEP), the best covariance function that can be used for prediction is Quadratic Exponential ARD (Automatic Relevance Determination) with RMSEP value 123,63. The highest prediction of the monthly rainfall is in January 2014 reached into 336,5 mm and the lowest in August 2014 with 36,94 mm.
Key Words: Gaussian Procces Regression, Covariance Function, Rainfall Prediction
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7652
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/36850
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
MODELING OF LOCAL POLYNOMIAL KERNEL NONPARAMETRIC REGRESSION FOR COVID DAILY CASES IN SEMARANG CITY, INDONESIA
Utami, Tiani Wahyu
Program Study of Statistics, Universitas Muhammadiyah Semarang
Lahdji, Aisyah
Medical Faculty, University Muhammadiyah Semarang
COVID; generalized cross validation; local polynomial kernel; nonparametric regression
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was recently discovered. Coronavirus disease is now a pandemic that occurs in many countries in the world, one of which is Indonesia. One of the cities in Indonesia that has found many COVID cases is Semarang city, located in Central Java. Data on cases of COVID patients in Semarang City which are measured daily do not form a certain distribution pattern. We can build a model with a flexible statistical approach without any assumptions that must be used, namely the nonparametric regression. The nonparametric regression in this research using Local Polynomial Kernel approach. Determination of the polynomial order and optimal bandwidth in Local Polynomial Kernel Regression modeling use the GCV (Generalized Cross Validation) method. The data used this research are data on the number of COVID patients daily cases in Semarang, Indonesia. Based on the results of the application of the COVID patient daily cases in Semarang City, the optimal bandwidth value is 0.86 and the polynomial order is 4 with the minimum GCV is 3179.568 so that the model estimation results the MSE is 2922.22 and the determination coefficient is 97%. The estimation results show the highest number of Corona in the Semarang City at the beginning of July 2020. After the corona case increased in July, while the corona case in August decreased.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36850
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/36850/110973
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/9201
2018-02-27T10:17:39Z
media_statistika:ART
nmb a2200000Iu 4500
"150630 2015 eng "
2477-0647
1979-3693
dc
OPTIMISASI MULTIOBJEKTIF UNTUK PEMBENTUKAN PORTOFOLIO
Hoyyi, Abdul
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Ispriyanti, Dwi
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Investing in asset such as stock; besides generate profit (return), it is also deal with a risk of loss, so that portofolio diversification is needed to reduce the risk. In the establishment of stock portofolio, the investors seeking to maximize the expected return of investment with a certain level of risk that still can be accepted. Portofolios that can achieve the above objectives called optimal portofolios. The application of multiobjective optimization on the establishment of the optimal portofolio is to maximize the return and minimize the risk at the same time. The aim of this research is to analize the proportion of each stock in order to form an optimal portofolio and to analyze the level of benefits and risks of the portofolio which is formed in accordance with the preferences of investors. The data used are monthly stock data of ASII, TLKM, SMGR, LPKR and BBNI. The optimal portofolio for risk seeker investors is a portofolio that used coefficient k =0,01, namely by investing in SMGR whilst the optimal portofolio for risk indifference investors is a portfolia which has coefficient 1 ≤ k ≤ 100 namely by investing in ASII, TLKM, SMGR, LPKR, and BBNI. Whereas, the optimal portofolio for risk averse investors is a portfolio which has coefficient k =1000 that is by investing in ASII, TLKM, SMGR, LPKR, and BBNI.
Keywords: Portofolio, Multi Objective Optimization
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9201
MEDIA STATISTIKA; Vol 8, No 1 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/60002
2024-02-26T03:51:54Z
media_statistika:ART
nmb a2200000Iu 4500
"240226 2024 eng "
2477-0647
1979-3693
dc
PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY
Rahmanda, Lalu Ramzy
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Fernandes, Adji Achmad Rinaldo
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Solimun, Solimun
Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University
Ramifidiosa, Lucius
Department of Mathematical Informatics, University of Antsiranana, Madagascar
Zamelina, Armando Jacquis Federal
Department of Statistics, Brawijaya University
Artificial Neural Network; GLMM; Mental Health; Longitudinal Data Analysis; Pulmonary Tuberculosis
Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-12-22 11:43:06
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/60002
MEDIA STATISTIKA; Vol 16, No 2 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/32637
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
MEASUREMENT OF SUPPORT VECTOR REGRESSION PERFORMANCE WITH CLUSTER ANALYSIS FOR STOCK PRICE MODELING
Arsy, Izza Dinikal
Statistics Study Program, Universitas Gadjah Mada, Indonesia
Rosadi, Dedi
Statistics Study Program, Universitas Gadjah Mada, Indonesia https://orcid.org/0000-0003-2689-253X
Support Vector Regression; Stock; Cluster; Volatility.
Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volatility. This case study makes use of daily closing price data from 10 LQ-45 index shares from October 12, 2018 to October 11, 2019. In conclusion, SVR's performance on stocks with high volatility produces RMSE, which is considerably higher than SVR's performance on stocks with low volatility.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/32637
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
ind
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/13128
2018-04-08T20:45:28Z
media_statistika:ART
nmb a2200000Iu 4500
"161230 2016 eng "
2477-0647
1979-3693
dc
PEMODELAN REGRESI NONPARAMETRIK MENGGUNAKAN PENDEKATAN POLINOMIAL LOKAL PADA BEBAN LISTRIK DI KOTA SEMARANG
Suparti, Suparti
Departemen Statistika, Universitas Diponegoro
Prahutama, Alan
Departemen Statistika, Universitas Diponegoro
Semarang is the provincial capital of Central Java, with infrastructure and economic’s growth was high. The phenomenon of power outages that occurred in Semarang, certainly disrupted economic development in Semarang. Large electrical energy consumed by industrial-scale consumers and households in the San Francisco area, monitored or recorded automatically and presented into a historical data load power consumption. Therefore, this study modeling the load power consumption at a time when not influenced by the use of electrical load (t-1)-th. Modeling using nonparametric regression approach with Local polynomial. In this study, the kernel used is a Gaussian kernel. In local polynomial modeling, determined optimum bandwidth. One of the optimum bandwidth determination using the Generalized Cross Validation (GCV). GCV values obtained amounted to 1425.726 with a minimum bandwidth of 394. Modelling generate local polynomial of order 2 with MSE value of 1408.672.
Keywords: electrical load, local polinomial, gaussian kernel, GCV.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-12-24 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/13128
MEDIA STATISTIKA; Vol 9, No 2 (2016): Media Statistika
eng
Copyright (c) 2016 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18275
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
PEMETAAN TINDAK KRIMINAL DI WILAYAH MADIUN DENGAN ANALISIS KORESPONDENSI BERGANDA
Nufitasari, Anindia
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Wijayanto, Hari
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Sulvianti, Itasia Dina
Fakultas MIPA, Institut Pertanian Bogor (IPB)
Multiple correspondence analysis is one of the statistical methods that can be used to describe the characteristics of criminal acts. Information about charateristic of crime is necessary to reduce the number of criminal acts. The purpose of this study is to describe criminal acts in the City and District of Madiun. Data sourced from Madiun City Police Station and Madiun District Police recording during 2016. The result of the research shows that criminal acts of theft, mistreatment and fraud have similar events characteristic at public place and residential area of Madiun City and Sub Unit of Development Area 2 at 06.00-11.59. Gambling and rape also characterize similar events at the crime scene residential area Sub Unit of Development Area 3 at 18.00-23.59. Illegal logging has different characteristics than other criminal acts. Meanwhile, theft, torture, and rape have characteristics of ≤ 25 years old and education level ≥ Senior High School (SHS). But criminal acts of fraud, illegal logging, and gambling have different characteristics of the perpetrators.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18275
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2495
2020-10-01T08:25:13Z
media_statistika:ART
nmb a2200000Iu 4500
"091229 2009 eng "
2477-0647
1979-3693
dc
PEMILIHAN THRESHOLD OPTIMAL PADA ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN METODE CROSS VALIDASI
Suparti, Suparti
Tarno, Tarno
Hapsari, Paula Meilina Dwi
If x is a predictor variable and y is a response variable of the regression model y = f (x)+ Î with f is a regression function which not yet been known and Î is independent random variable with mean 0 and variance , hence function f can be estimated by parametric and nonparametric approach. In this paper function f is estimated with a nonparametric approach. Nonparametric approach that used is a wavelet shrinkage or a wavelet threshold method. In the function estimation with a wavelet threshold method, the value of threshold has the most important role to determine level of smoothing estimator. The small threshold give function estimation very no smoothly, while the big value of threshold give function estimation very smoothly. Therefore the optimal value of threshold should be selected to determine the optimal function estimation. One of the methods to determine the optimal value of threshold by minimize a cross validation function. The cross validation method that be used is two-fold cross validatiaon. In this cross validation, it compute the predicted value by using a half of data set. The original data set is split into two subsets of equal size : one containing only the even indexed data, and the other, the odd indexed data. The odd data will be used to predict the even data, and vice versa. Based on the result of data analysis, the optimal threshold with cross validation method is not uniq, but they give the uniq of wavelet thersholding regression estimation.
Keywords : Nonparametric Regression, Wavelet Threshold Estimator, Cross Validation.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2495
MEDIA STATISTIKA; Vol 2, No 2 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20473
2023-12-12T02:27:00Z
media_statistika:FMT
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
Front Matter
Statistika, Media
Cover dan Daftar Isi Vol. 11 No. 1 Juni 2018
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20473
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2512
2020-10-01T08:28:30Z
media_statistika:ART
nmb a2200000Iu 4500
"101228 2010 eng "
2477-0647
1979-3693
dc
ANALISIS KLASTER UNTUK SEGMENTASI PEMIRSA PROGRAM BERITA SORE STASIUN TV SWASTA
Rosiatun, Aan
Widiharih, Tatik
Safitri, Diah
A procedure market segmentation is designing the market segmentation use the method of cluster k-means analyze which applied in process designing the market evening news audiences on tv chanels. The process of grouping audiences into each segment which formed, based on likeness of characteristic owned and it formed 3 market segment evening news audiences, that is audiences group who give low evaluation, audiences group who give enough evaluation, and audiences group who give high evaluation. Result from the market segmentation with case study at Pangkah district Tegal regency got first cluster is 25.2 %, second cluster is 46 %, and third cluster is 28.8 %. Marketing strategy can target be old > 20 years because it has members total of cluster is biggest. The result can be used by a television company to determine marketing strategy.
Keywords: Characteristic, Market Segmentation, Cluster K-Means Analysis
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-12-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2512
MEDIA STATISTIKA; Vol 3, No 2 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/22775
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
PERBANDINGAN MODEL CAPITAL ASSET PRICING MODEL (CAPM) DAN LIQUIDITY ADJUSTED CAPITAL ASSET PRICING MODEL (LCAPM) DALAM PEMBENTUKAN PORTOFOLIO OPTIMAL SAHAM SYARIAH
Apriyanti, Veladita
UIN Sunan Kalijaga
Supandi, Epha Diana
UIN Sunan Kalijaga
In stock investments, every investor wants to get a high level of return and low risk. The stock price is very volatile and unpredictable, this makes investors have to find solutions in order to get a benefit from this investment. One way is to form a portfolio. A portfolio is a collection of several shares. There are several models for calculating stock portfolios such as CAPM (Capital Asset Pricing Model) and LCAPM (Liquidity Adjusted Capital Asset Pricing Model). The CAPM is a model that describes the relationship between the expected return and risk of investing in a security. The LCAPM is an extension of CAPM by taking into account the liquidity of assets. Data from Jakarta Islamic Index is used to verify the two models. In this case, the empirical results show that the performance of CAPM is better than the LCAPM.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/22775
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2631
2020-10-01T08:11:29Z
media_statistika:ART
nmb a2200000Iu 4500
"080630 2008 eng "
2477-0647
1979-3693
dc
RANCANGAN STRIP PLOT MODEL TETAP
Wuryandari, Triastuti
Wilandari, Yuciana
Afifah, Noor
The experiment involve the study of the effects of two or more factors can be used the factorial designs. The factorial designs have several advantages. They are more efficient than one factor at a time experints. Furthermore, a factorial designs is necessary when interaction may be present to avoid misleading conclutions. In the Strip Plot design is factorial two factors which random factors aren’t based on main plot or the whole plot but the important is it’s interaction. There are three error in the Strip plot. They are error caused by factor A, error caused by factor B and error by A and B interaction.
Keywords: Factorial, Strip Plot, Interaction
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2631
MEDIA STATISTIKA; Vol 1, No 1 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/20027
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
COMPARISON OF ARIMA, TRANSFER FUNCTION AND VAR MODELS FOR FORECASTING CPI, STOCK PRICES, AND INDONESIAN EXCHANGE RATE: ACCURACY VS. EXPLAINABILITY
Purwa, Taly
Badan Pusat Statistik (BPS) Provinsi Bali http://orcid.org/0000-0001-8730-4821
Nafngiyana, Ulin
Badan Pusat Statistik (BPS) Kabupaten Trenggalek
Suhartono, Suhartono
Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember (ITS) http://orcid.org/0000-0002-4194-7220
CPI; Stock Price; Exchange Rate; ARIMA; Transfer Function; VAR
The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/20027
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/20027/91192
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/36772
2022-01-09T04:26:08Z
media_statistika:FMT
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
Front Matter Vol. 13 No. 2 2020
Statistika, Media
Cover dan Daftar Isi Media Statistika Vol. 13 No. 2 Desember 2020
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36772
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
eng
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/7638
2016-03-15T17:19:32Z
media_statistika:ART
nmb a2200000Iu 4500
"131227 2013 eng "
2477-0647
1979-3693
dc
NEW METHOD TO MINING ASSOCIATION RULES USING MULTI-LAYER MATRIX QUADRANT
Hakim, R. B. Fajriya
Successful retail organizations utilizing any information they had for managing sales strategies. Most of information about consumer’s retail organization had been stored in transactions database. Discovering knowledge from information stored in the transaction database has led several established methods implemented in many cases with their advantages and disadvantages. One of methodologies to uncover relationship among frequent items purchased in transaction database known as association rules. However, the research of association rules techniques to find knowledge from transaction database still provides a significant opportunity for new methods to participate. In this paper, we proposed a new method of mapping a frequent item set to a multi-layer matrix quadrant. This new method could show the metrics usually used to describe the association rules between items purchased same as any method used in association rules analysis.
Keywords: Association Rules, Matrix Quadrant, Support, Confidence, Lift Ratio
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7638
MEDIA STATISTIKA; Vol 6, No 2 (2013): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/36501
2022-01-12T00:49:36Z
media_statistika:ART
nmb a2200000Iu 4500
"220111 2022 eng "
2477-0647
1979-3693
dc
GSTARI-ARCH MODEL AND APPLICATION ON POSITIVE CONFIRMED DATA FOR COVID-19 IN WEST JAVA
Alawiyah, Mutik
Master of Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran
Kusuma, Dianne Amor
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran http://math.fmipa.unpad.ac.id
Ruchjana, Budi Nurani
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran http://math.fmipa.unpad.ac.id/ http://orcid.org/0000-0001-7580-604X
ARCH; GSTARI; Positive Covid-19 Confirmed Cases; RMSE
Time series model that is commonly used is the Box-Jenkins based time series model. Time series data phenomena based on Box-Jenkins can be combined with spatial data, it is called the space time model One model based on Box-Jenkins model with heterogeneous location characteristics is the Generalized Space Time Autoregressive Integrated (GSTARI) model for a model that assumes data is not stationary or has a trend. This paper discusses the development of the GSTARI model with the assumption that the error variance is not constant which is applied to positive data confirmed by Covid-19 in West Java Province, especially in 4 regencies/cities that have cases in the high category from 6 March 2020 until 31 December 2020. Four regencies/cities are Depok City, Bekasi City, Bekasi Regency, and Karawang Regency. Parameter estimation method for the assumption of non-constant error variance can use Autoregressive Conditional Heteroscedasticity (ARCH) method. GSTARI-ARCH modeling procedure followed three Box-Jenkins stages, namely the identification process, parameter estimation and checking diagnostic. Application of the GSTARI-ARCH Model to Covid-19 positive confirmed data in 4 regencies/cities has a minimum value of RMSE in Bekasi City. The plot of forecast results for the four regencies/cities has a similar pattern to the actual data only applicable for a short time for 1-2 days.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-01-11 13:52:25
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/36501
MEDIA STATISTIKA; Vol 14, No 2 (2021): Media Statistika
eng
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8493
2018-02-27T11:02:45Z
media_statistika:ART
nmb a2200000Iu 4500
"141228 2014 eng "
2477-0647
1979-3693
dc
PEMODELAN INFLASI BERDASARKAN HARGA-HARGA PANGAN MENGGUNAKAN SPLINE MULTIVARIABEL
Prahutama, Alan
Jurusan Statistika, FSM, Universitas Diponegoro
Utama, Tiani Wahyu
Jurusan Statistika, Universitas Muhammadiyah Semarang
Caraka, Rezzy Eko
Jurusan Statistika, FSM, Universitas Diponegoro
Zumrohtuliyosi, Dede
Jurusan Statistika, FSM, Universitas Diponegoro
Inflation is defined as a sustained increase in the general level of price for goods and services. Some of the events that led to inflation in Indonesia is rising fuel prices, rising prices of meat and chili. Inflation has negative impact, because decreased purchasing power. So that the inflation model is needed. Modeling inflation can be use regression models. The approach can be performed with nonparametric regression, one of method of nonparametric regression is spline method. In this case, use three predictors to modeling inflation using spline multivariable. The predictors are price of rice, price of chicken, and price of chili. Obtained multivariable spline models with R-square of 93.94% with optimal m = 2 (quadratic) for 1 knots.
Keywords: Spline Multivariable, GCV, Inflation
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8493
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/59119
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"231207 2023 eng "
2477-0647
1979-3693
dc
SURVIVAL ANALYSIS FOR RECURRENT EVENT DATA USING COUNTING PROCESS APPROACH: APPLICATION TO DIABETICS
Wuryandari, Triastuti
Department of Statistics, Universitas Diponegoro
Wilandari, Yuciana
Department of Statistics, Universitas Diponegoro
Survival Analysis; Diabetics; Cox Model; Recurrent Event; AG Model
Survival analysis is a branch of statistics for analyzing the duration of time until one or more events occur. Time to recurrence of diabetics including survival data. Diabetes can’t be cured but it can be controlled. Diabetics who don’t maintain their health and lifestyle will experience recurrence. Factors thought to influence the recurrence of diabetics are internal factors such as genetics and external factors such as lifestyle. The recurrence time of an object includes recurrent events because each object can experience the same recurrent event during the follow-up. One of the analysis to determine factors that are thought to influence the recurrence time of diabetics is survival analysis. Survival data can be modeled into a regression model if the survival time of an object is influenced by other factors. One of the regression models for survival data is Cox regression. One of the Cox regression models for recurrent event data is the AG model which uses a counting process approach. This study used data on the recurrence of diabetics at MH Thamrin Cileungsi Hospital. Based on data analysis, factors that influence the recurrence of diabetics are age, gender, and type of complication.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/59119
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/42755
2023-04-27T12:38:41Z
media_statistika:ART
nmb a2200000Iu 4500
"230406 2023 eng "
2477-0647
1979-3693
dc
COMPARISON OF SMOTE RANDOM FOREST AND SMOTE K-NEAREST NEIGHBORS CLASSIFICATION ANALYSIS ON IMBALANCED DATA
Prasetya, Jus
PROGRAM STUDI MAGISTER MATEMATIKA, Departemen Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gadjah Mada, Sekip Utara BLS 21 Yogyakarta 55281
Abdurakhman, Abdurakhman
Department of Mathematics, Gadjah Mada University, Indonesia
Machine Learning; Classification; SMOTE; Random Forest; k-Nearest Neighbors
In machine learning study, classification analysis aims to minimize misclassification and also maximize the results of prediction accuracy. The main characteristic of this classification problem is that there is one class that significantly exceeds the number of samples of other classes. SMOTE minority class data is studied and extrapolated so that it can produce new synthetic samples. Random forest is a classification method consisting of a combination of mutually independent classification trees. K-Nearest Neighbors which is a classification method that labels the new sample based on the nearest neighbors of the new sample. SMOTE generates synthesis data in the minority class, namely class 1 (cervical cancer) to 585 observation respondents (samples) so that the total observation respondents are 1208 samples. SMOTE random forest resulted an accuracy of 96.28%, sensitivity 99.17%, specificity 93.44%, precision 93.70%, and AUC 96.30%. SMOTE K-Nearest Neighborss resulted an accuracy of 87.60%, sensitivity 77.50%, specificity 97.54%, precision 96.88%, and AUC 82.27%. SMOTE random forest produces a perfect classification model, SMOTE K-Nearest neighbors classification produces a good classification model, while the random forest and K-Nearest neighbors classification on imbalanced data results a failed classification model.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-04 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/42755
MEDIA STATISTIKA; Vol 15, No 2 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/11721
2018-04-07T21:59:05Z
media_statistika:ART
nmb a2200000Iu 4500
"160630 2016 eng "
2477-0647
1979-3693
dc
EFEK DIAMETER COIL, PERBANDINGAN JUMLAH LILITAN, JENIS COIL, PADA TRASMITTER RECEIVER TERHADAP EFISIENSI ENERGI TRANSFER WIRELESS TRANSFER ELECTRICITY DENGAN METODE DESAIN OF EXPERIMENT (DOE)
Winarso, Kukuh
Program Studi Teknik Industri, Fakultas Teknik, Universitas Trunojoyo Madura
Alfaris, Salman
Program Studi Teknik Industri, Fakultas Teknik, Universitas Trunojoyo Madura
Wireless power transfer is an alternative distribution of electrical power without a physical relationship with the cable. In the study took a problem concerning the design series of receiver on transfer system power without wires that have not done research on the effect of its components such as the diameter of the coil, the ratio of the number of windings, and the type of coil. The components used as an experiment to determine the efficiency of energy transfer from the result electric power. The purpose of this study is used to determine whether the components or factors such as the diameter of the coil, the ratio of the number of windings, and the type of coil give effect to the energy transfer efficiency of the electrical power produced. Research conducted an experiment using a factorial design experiments 23 to solve this problem. Materials used and also used as a factor in the study include the diameter of the coil, the ratio of the number of windings, and coil types, and each factor has two levels. The experimental results showed that factors coil diameter, number of turns ratio and type of coil influence on the efficiency of energy transfer. Decision-making is seen from the results of the calculation of the value of F count greater than F table values.
Keywords: Wireless Power Transfer, The Efficiency of Energy Transfer, Factorial
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2016-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/11721
MEDIA STATISTIKA; Vol 9, No 1 (2016): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/18277
2023-12-12T02:27:03Z
media_statistika:ART
nmb a2200000Iu 4500
"171230 2017 eng "
2477-0647
1979-3693
dc
MODEL ANTREAN NORMAL DAN TRIANGULAR (Studi Kasus : Gerbang Tol Tembalang Semarang)
Sugito, Sugito
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
The growing number of vehicle in each year resulting an inevitable congestion, one of them is jamming vehicle transaction in Tembalang toll gate. This condition can cause dissatisfaction to the toll road users in obtaining services. It is need to be specified the appropriate queue system model to the conditions of service in Tembalang toll gate. So it can be determined the number of booth service is working optimally. Based on the data analysis obtained from the Arena software, the queue system model that can describe the conditions of service at Tembalang toll gates with data total- time, time-total, and time-time the direction of Srondol-Jatingaleh at the regular toll booth is (Norm/G/2):(GD/∞/∞), (G/Norm/2): (GD/∞/∞), (G/G/2): (GD/∞/∞) and at the automatic toll booth is (G/Tria/3): (GD/∞/∞), (Tria/G/3): (GD/∞/∞), (G/G/3): (GD/∞/∞) while with the direction of Jatingaleh-Srondol at the regular toll booth is (Norm/G/3): (GD/∞/∞), (G/Norm/3): (GD/∞/∞), (G/G/3): (GD/∞/∞) and (G/Tria/2): (GD/∞/∞), (Tria/G/2): (GD/∞/∞), (G/G/2): (GD/∞/∞) at automatic toll booth.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18277
MEDIA STATISTIKA; Vol 10, No 2 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2479
2020-10-01T08:23:52Z
media_statistika:ART
nmb a2200000Iu 4500
"090623 2009 eng "
2477-0647
1979-3693
dc
ESTIMASI REGRESI NON PARAMETRIK DENGAN METODE WAVELET SHRINKAGE NEURAL NETWORK PADA MODEL RANCANGAN TETAP
Yasin, Hasbi
If X is a predictor variable and Y is a response variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network method. At this method, the smoothly function estimation is depending on shrinkage parameter’s that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation.
Keywords: Nonparametric Regression, Wavelet Shrinkage Neural Network
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2009-06-23 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2479
MEDIA STATISTIKA; Vol 2, No 1 (2009): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/19611
2023-12-12T02:27:00Z
media_statistika:ART
nmb a2200000Iu 4500
"180929 2018 eng "
2477-0647
1979-3693
dc
Abdurakhman, Abdurakhman
Departemen Matematika, FMIPA Universitas Gadjah Mada https://scholar.google.co.id/citations?user=3PZiQs4AAAAJ&hl=id
Maruddani, Di Asih I
The Gram-Charlier expansion, where skewness and kurtosis directly appear as parameters, has become popular in finance as a generalization of the normal density. Non-normal skewness and kurtosis of underlying asset of bond issuer company are significantly contributes to the phenomenon of volatility smile. Hermite polynomial is used to get an expansion of the probability distribution. In this paper, Gram-Charlier model is applied to BTPN Bond which is issued in 2017. The result showed that Gram-Charlier model is more consistent than Black-Scholes model when the skewness and kurtosis are taken into account.
Keywords: Skewness, Kurtosis, Gram-Charlier, Hermite polynomial
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-09-29 08:25:53
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/19611
MEDIA STATISTIKA; Vol 11, No 1 (2018): Media Statistika
ind
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2507
2020-10-01T08:30:56Z
media_statistika:ART
nmb a2200000Iu 4500
"110629 2011 eng "
2477-0647
1979-3693
dc
PEMODELAN KURVA IMBAL HASIL DAN KOMPUTASINYA DENGAN PAKET SOFTWARE RCMDRPLUGIN.ECONOMETRICS
Rosadi, Dedi
In this paper discussed the yield curve modeling methodology using the Nelson-Siegel model Svenson (Svensson, 1994) with special application to model the Indonesian Government Securities Yield Curve. The focus of this study is the computation of the yield curve model using the R, especially using a tool called the R-GUI RcmdrPlugin.Econometrics (Rosadi, 2011). For the empirical illustration, also given examples of applications using real data from the Indonesian capital market.
Keywords: Kurva yield, R-GUI, Nelson-Siegel-Svenson
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-06-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2507
MEDIA STATISTIKA; Vol 4, No 1 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/19641
2022-07-27T00:34:48Z
media_statistika:ART
nmb a2200000Iu 4500
"190724 2019 eng "
2477-0647
1979-3693
dc
DIAGRAM KENDALI MEWMV DAN MEWMA BERBASIS MODEL TIME SERIES PADA DATA BERAUTOKORELASI: STUDI KASUS GULA KRISTAL PUTIH
Suhermi, Novri
Institut Teknologi Sepuluh Nopember https://sites.google.com/statistika.its.ac.id/novrisuhermi http://orcid.org/0000-0002-8016-5803
Puspitaningrum, Retno
Institut Teknologi Sepuluh Nopember
Suharsono, Agus
Institut Teknologi Sepuluh Nopember
In this study, we aim to build a multivariate control chart for autocorrelated data. We use MEWMA and MEWMV control charts which are free of normality assumption. Time series model is then applied to tackle autocorrelation problem in the data where the control charts require independence assumption. The real dataset used is the quality characteristics of white crystal sugar, also called gula kristal putih (GKP). There are 3 quality characteristics of GKP, namely moisture (%), color of solution (IU), and grain type (mm). It is considered that these quality characteristics are correlated each other. Our results show that the variability process is out of control where there are 5 observations outside the control limits. Meanwhile the mean process is also out of control. The factors causing the out of control include the workers, the raw materials, the measurement, the machines, and the methods. The process capability indices result in the values less than 1 which means the process is not sufficiently capable.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-07-24 20:50:55
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/19641
MEDIA STATISTIKA; Vol 12, No 1 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2608
2020-10-01T08:14:03Z
media_statistika:ART
nmb a2200000Iu 4500
"081230 2008 eng "
2477-0647
1979-3693
dc
PROSES INFERENSI PADA MODEL LOGIT
Rusgiyono, Agus
Let represent the response on a nominal random variable of Bernoulli distribution, with , where is a parameter with unknown value. Problems of estimating used smallest square methods in linier regression model can overcome with used maximum likelihood method in logistic regression..
Suppose is
maksimum likelihood estimstors of . In case can be obtained from first condition, ln(Ln(p)) to be maximum at point then be obtained and
that is unbiased estimator because
To be test hipothesis that , with a large sample size used fact that
Keywords : Estimator, unbiased estimator, test statistic
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2008-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2608
MEDIA STATISTIKA; Vol 1, No 2 (2008): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/26065
2022-07-27T00:34:36Z
media_statistika:ART
nmb a2200000Iu 4500
"200626 2020 eng "
2477-0647
1979-3693
dc
INTERPOLASI KRIGING DALAM PEMODELAN GSTAR-SUR DAN GSTARX-SUR PADA SERANGAN HAMA PENGGEREK BUAH KOPI
Pramoedyo, Henny
Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya
Ashari, Arif
Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya
Fadliana, Alfi
Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya
GSTAR; GSTARX; Forecasting; Kriging Interpolation; Coffee Berry Borer
The GSTAR and GSTARX models with the SUR approach normally can only be used in forecasting an event in the future in locations where the data is indeed used in forming the model. The problem that sometimes occurs in some cases is that not all locations that want to be modeled do not have data, or if there is data, the data is not as complete as other locations. This study uses GSTAR and GSTARX modeling with the SUR approach and combines them with the kriging interpolation technique in forecasting in an unobserved location. The case study used in this research is PBKo attack forecasting in Probolinggo Regency, where it is simulated that Watupanjang Village is an unobserved location because the location of coffee plantations in the area is difficult to reach due to difficult terrain / access roads. The results showed that PBKo pest attacks in the Probolinggo Regency could be predicted using the GSTAR model (1, [1,12]) and the GSTARX model (1, [1.12]) (10,0,0). Both models, both GSTAR Kriging and GSTARX Kriging, can be relied upon as an alternative to predicting PBKo pests in unobserved locations or where insufficient data are available.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-06-26 18:16:28
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/26065
MEDIA STATISTIKA; Vol 13, No 1 (2020): Media Statistika
ind
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/26065/91144
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/29303
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE USING VECTOR INTEGRATED MOVING AVERAGE
Trimono, Trimono
Magister of Mathematics, Institut Teknologi Bandung
Sonhaji, Abdulah
Program Study of Mathematics, Institut Teknologi Bandung
Mukhaiyar, Utriweni
Program Study of Mathematics, Institut Teknologi Bandung
Farmer Exchange Rate; Vector Time Series; VIMA(2,1); MAPE
Farmer Exchange Rate (FER) is an indicator that can be used to measure the level of farmers welfare. For every agriculture sector, FER is affected by the historical price of harvest from the corresponding sector and historical prices of other agriculture sectors. In Central Java Province, rice & palawija, horticulture, and fisheries are the largest agriculture sectors which is the main livelihood for most of the population. FER forecasting is a crucial thing to determine the level of farmers welfare in the future. One method that can be used to predict the value of a variable that is influenced by the historical value of several variables is Vector Time Series. An empirical study was conducted using FER data from the rice & palawija, horticulture and fisheries sectors for January 2011-June 2017 in Central Java Province. The results obtained show that by using the VIMA(2.1) model, the FER prediction was very accurate, with MAPE values were 1.91% (rice & palawija sector), 2.44% (horticulture sector), and 2.18% (fisheries sector).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/29303
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/23603
2022-01-09T04:26:08Z
media_statistika:ART
nmb a2200000Iu 4500
"201228 2020 eng "
2477-0647
1979-3693
dc
IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province)
Fadliana, Alfi
Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Raden Rahmat
Pramoedyo, Henny
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya
Fitriani, Rahma
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya
Stunting; Multicollinearity; Geographically Weighted Regression; Locally Compensated Ridge
East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2020-12-28 14:51:13
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/23603
MEDIA STATISTIKA; Vol 13, No 2 (2020): Media Statistika
ind
Copyright (c) 2020 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/5664
2020-09-30T17:36:00Z
media_statistika:ART
nmb a2200000Iu 4500
"130630 2013 eng "
2477-0647
1979-3693
dc
ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE
Suparti, Suparti
The inflation data is one of the financial time series data that has a high volatility, so if the data is modeled with parametric models (AR, MA and ARIMA), sometimes occur problems because there was an assumption that cannot be satisfied. The developed model of parametric to cope with the volatility of the data is the ARCH and GARCH models. This alternative parametric models still requires the normality assumption in the data that often cannot be satisfied by financial data. Then a nonparametric method that does not require strict assumptions as parametric methods is developed. This research aims to conduct a study in Indonesia inflation data modeling using nonparametric methods is spline regression model with truncated spline bases. Goodness of a spline regression model is determined by an orde and knots location . However, the knots location are more dominant in spline regression model. One way to get the optimal knots location are by minimizing the value of Generalized Cross Validation (GCV). By modeling the annual inflation data of Indonesia in December 2006 - December 2011, the inflation target in 2012 is 4.5% + 1% can be achieved while the inflation target in 2013 is 4.5% + 1% cannot be achieved, because that prediction in 2013 is 8.55%. It was caused by government policy to raise the price of basic electricity and the fuel prices in 2013.
Keywords : Inflation, Spline Regression Model, Generalized Cross Validation.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2013-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/5664
MEDIA STATISTIKA; Vol 6, No 1 (2013): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/28273
2021-06-30T10:13:33Z
media_statistika:ART
nmb a2200000Iu 4500
"210630 2021 eng "
2477-0647
1979-3693
dc
FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD
Atmaja, Dinul Darma
Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University
Widowati, Widowati
Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University
Warsito, Budi
Department of Statistics, Faculty of Science and Mathematics, Diponegoro University
Forecasting; Multivariate Time Series Analysis; VARMA, Indonesia Stock Exchange (IDX); LQ45.
Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2021-06-30 08:54:47
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/28273
MEDIA STATISTIKA; Vol 14, No 1 (2021): Media Statistika
eng
https://ejournal.undip.ac.id/index.php/media_statistika/article/download/28273/81319
Copyright (c) 2021 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/8293
2016-03-15T17:20:07Z
media_statistika:ART
nmb a2200000Iu 4500
"140630 2014 eng "
2477-0647
1979-3693
dc
BIPLOT UNTUK MENGETAHUI KARAKTERISTIK KABUPATEN/KOTA DI JAWA TENGAH BERDASARKAN PRODUKSI BAWANG PUTIH, BAWANG MERAH, CABE BESAR DAN CABE RAWIT
Safitri, Diah
Suparti, Suparti
Pratiwi, Esti
Estiningrum, Tyas
Biplot is a graphical representation of a data matrix. Garlic, onions, chili, and thai pepper are important plant in Indonesia because most people in Indonesia especially in Central Java consume garlic, onions, chili, and thai pepper every day. In this research, districts in Central Java seen characteristics are based on the productions of garlic, onions, chili, and thai pepper using biplot. There are highly correlation between chili and thai pepper, which means districts that have highly productions of chili will also tend to have highly production of thai pepper. There are some districts have the production of garlic, onions, chili, and thai pepper relatively low, and there are some of the city has zero production of garlic, onions, chili, and thai pepper.
Keywords: Biplot, Production of garlic, onions, chili, thai pepper
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/8293
MEDIA STATISTIKA; Vol 7, No 1 (2014): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/47210
2023-12-22T11:43:33Z
media_statistika:ART
nmb a2200000Iu 4500
"230609 2023 eng "
2477-0647
1979-3693
dc
MODELING OF FARMER EXCHANGE RATE IN ACEH PROVINCE USING LONGITUDINAL DATA ANALYSIS
Miftahuddin, Miftahuddin
Department of Statistics, Universitas Syiah Kuala https://orcid.org/0000-0002-6414-4498
Husna, Ziqratul
Department of Statistics, Universitas Syiah Kuala, Indonesia
Gunawan, Eddy
Department of Economy and Development, Universitas Syiah Kuala
Muchtar, Syawaliah
Department of Chemical Engineering, Universitas Syiah Kuala
FER; IR; IP; CPI; Inflation
Farmer's Exchange Rate (FER) is one indicator to see the level of farmers' welfare. From 2014 to 2020, Aceh Province's FER was below 100 which indicates that farmers have not yet reached the level of welfare. This happens because of various factors including the price received by farmers (IR) is smaller than the price paid by farmers (IP). To find out the factors that influence the FER, it is necessary to do an analysis by forming a model. In this study, modeling of the FER data will be carried out, and see the factors that influence the index number with the longitudinal data regression approach. There are three estimation models, i.e. Common Effect Model, Fixed Effect Model, and Random Effect Model. Model selection of the best model is by using the Chow, Hausman, and Lagrange Multiplier tests. Furthermore, test the significance of the parameters using the simultaneous and partial tests and also see the value of the coefficient of determination (R2). The results obtained indicate that the appropriate model for the IR and IP data is the Random Effect Model where the R2for the IR and IP models are 67.06% and 85.42 respectively.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2023-04-27 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/47210
MEDIA STATISTIKA; Vol 16, No 1 (2023): Media Statistika
eng
Copyright (c) 2023 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/43085
2022-07-28T02:52:58Z
media_statistika:ART
nmb a2200000Iu 4500
"220727 2022 eng "
2477-0647
1979-3693
dc
EFFECT SERVICE QUALITY AND CUSTOMER VALUE TO CUSTOMER LOYALTY THROUGH CUSTOMER SATISFACTION USE OF DAMRI TRANSPORTATION MODE IN BANDUNG
Mohamad, Dadang
Civil Engineering Education, Universitas Pendidikan Indonesia
Laksito, Grida Saktian
Research Collaboration Community
Sukono, Sukono
Department of Mathematics, Universitas Padjadjaran
DAMRI Transportation; Service Quality; Customer Value; Customer Loyalty; Customer Satisfaction; SEM AMOS.
This study aims to determine how the influence of service quality and customer value on customer loyalty through customer satisfaction on DAMRI Transport Mode in Bandung, The research method used is quantitative, the sampling technique uses non-probability sampling and a sample of 260 respondents is obtained, the analytical tool used is Path Analysis and hypotheses using a significance test using the SPSS Version 24 and SEM AMOS analysis tool. The results of this study indicate that direct testing for direct testing of the customer loyalty variable it is found that service quality and customer satisfaction to customer loyalty has a positive and significant effect for use bus DAMRI in Bandung, while for customer value it has no effect on customer loyalty for use bus DAMRI in Bandung. With regard to customers' ownership, it is possible to increase the quality of service quality and customer loyalty to customers by giving goods a consumer satisfaction that would allow them to be loyal to using DAMRI bus as a mode of transportation in everyday activities.
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2022-07-27 01:17:05
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/43085
MEDIA STATISTIKA; Vol 15, No 1 (2022): Media Statistika
eng
Copyright (c) 2022 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/10084
2018-02-27T10:09:44Z
media_statistika:ART
nmb a2200000Iu 4500
"151230 2015 eng "
2477-0647
1979-3693
dc
KOMPARASI METODE PERAMALAN AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS DENGAN SINGLE EXPONENTIAL SMOOTHING
Endaryati, Betik
Jurusan Komputasi Statistik Sekolah Tinggi Ilmu Statistik (STIS) Jakarta
Kurniawan, Robert
Jurusan Komputasi Statistik Sekolah Tinggi Ilmu Statistik (STIS) Jakarta
Automatic clustering technique and fuzzy logical relationships(ACFLR) is one of the forecasting method that used to predict time series data that can be applied in any data. Several previous studies said that this method has a good accuracy. Therefore, this study aims to compare the ACFLR methods with single exponential smoothing method and apply it to simulation data with uniform distribution. The performance of the method is measured based on MSE and MAPE. The results of the comparison of the methods showed that ACFLR has a higher forecasting accuracy than single exponential smoothing. This is evidenced by the value of MSE and MAPE of ACFLR is lower than single exponential smoothing.
Keywords: Fuzzy, Forecasting, Automatic Clustering-Fuzzy Logic Relationships, Single Exponential Smoothing
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2015-12-26 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/10084
MEDIA STATISTIKA; Vol 8, No 2 (2015): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/15598
2018-04-07T09:20:32Z
media_statistika:ART
nmb a2200000Iu 4500
"170630 2017 eng "
2477-0647
1979-3693
dc
Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda
Sebayang, Jimmy Saputra
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik
Yuniarto, Budi
Jurusan Komputasi Statistik, Sekolah Tinggi Ilmu Statistik
Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is a free-distribution estimator because there's no assumptions that have to be satisfied. However, modeling data using ANN has some problems such as selection of network topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.
Keywords:
Neural Network, Genetic Algorithm, Ordinary Least Square
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2017-06-28 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/15598
MEDIA STATISTIKA; Vol 10, No 1 (2017): Media Statistika
eng
Copyright (c) 2017 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2470
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
PENANGANAN OVERDISPERSI PADA MODEL REGRESI POISSON MENGGUNAKAN MODEL REGRESI BINOMIAL NEGATIF
Simarmata, Rio Tongaril
Ispriyanti, Dwi
Poisson regression is the most popular tool for modeling the relationship between a discrete data in the response variable and a set of predictors with continue, discrete, categoric or mix data. Response variable with discrete data, however, may overdispersed or underdispersed, not conductive to Poisson regression which assumed that the mean value equals to variance (equidispersed). One of the model that be used to overdispersed the discrete data is a regression model based on mixture distribution namely Poisson-gamma mixture which result negative binomial distribution. This regression model usually known as binomial negative regression. Using Generalized Linier Model (GLM) approach, the given model, parameter estimate, diagnostics, and interpretation of negative binomial regression can be determined.
Keyword: Negative Binomial Distribution, Dispersion, Generalized Linier Model
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2470
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/18406
2018-04-04T11:30:34Z
media_statistika:FMT
nmb a2200000Iu 4500
"141230 2014 eng "
2477-0647
1979-3693
dc
Front-Matter
Statistika, Media
Cover dan Daftar Isi Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2014-12-31 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/18406
MEDIA STATISTIKA; Vol 7, No 2 (2014): Media Statistika
eng
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2502
2020-10-01T08:30:23Z
media_statistika:ART
nmb a2200000Iu 4500
"111229 2011 eng "
2477-0647
1979-3693
dc
PENDUGAAN DATA HILANG DENGAN MENGGUNAKAN DATA AUGMENTATION
Nova, Mesra
Mukid, Moch. Abdul
Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing data if the observed data are known. The next step is posterior or P-step where the estimation process of parameter values from the complete data is conducted. Imputation and posterior steps on the data augmentation will continue to run until the convergence is reached. The estimate of missing data is obtained through the average of simulated values.
Keywords: Missing Data, Data Augmentation, Imputation Step, Posterior Step
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2011-12-29 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2502
MEDIA STATISTIKA; Vol 4, No 2 (2011): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/19991
2022-07-27T00:34:53Z
media_statistika:ART
nmb a2200000Iu 4500
"181230 2018 eng "
2477-0647
1979-3693
dc
Afa, Ihdayani Banun
Suparti, Suparti
Rahmawati, Rita
The composite stock price index or Indonesia Composite Index (ICI) is a composite index of all stocks listed on the Indonesia Stock Exchange and its movements indicate conditions that occur in the capital market. For investors, the ICI movement is one of the important indicator to make a decision whether the stocks will be sold, held or bought new shares. The ICI movement (y) was influenced by several factors including Inflation (x1), Exchange Rate (x2) and SBI interest rate (x3). This study aims to compare the ICI modeling using the parameric and nonparametric approaches, namely multivariable linear regression and multivariable spline regression. Determination of the better model is based on the smaller MSE and the larger R2. The best regression model is multivariable spline regression with x1, x2 and x3, each with a sequence orde (3,2,2) and the number of knot points (1,2,2).
Keywords: Indonesia Composite Index, Multiple Linear Regression, Multivariable Spline Regression, MSE, R2
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2018-12-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/19991
MEDIA STATISTIKA; Vol 11, No 2 (2018): Media Statistika
ind
Copyright (c) 2018 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/2523
2020-10-01T08:27:38Z
media_statistika:ART
nmb a2200000Iu 4500
"100617 2010 eng "
2477-0647
1979-3693
dc
PENERAPAN REGRESI LOGISTIK MULTINOMIAL PADA PEMILIHAN ALAT KONTRASEPSI WANITA (Studi Kasus di Desa Tonggara Kecamatan Kedungbanteng Kabupaten Tegal)
Sulistio, Erna
Ispriyanti, Dwi
The development of a growing population, causing many problems within national development, so the program necessary to reduce the population of family planning program, one of the programs is Contraceptive Services. A variety of contraceptive choices provided by the government especially for women, including: pill, injection, IUD, implant, tissue KB, tubectomy, cream, jelly, and foam. The selection of contraceptives for women have to weigh various factors. So we want to know the factors which influence women in choosing a particular contraceptive. By testing the significance of the multinomial logistic regression model through the G test statistic can be shown there are four factors that influence contraceptive use, namely maternal age, number of living children, age of last child, and pregnancy plans.
Keywords: Contraception, Multinomial Logistic Regression
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2010-06-17 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2523
MEDIA STATISTIKA; Vol 3, No 1 (2010): Media Statistika
eng
Copyright (c)
oai:ojs.ejournal.undip.ac.id:article/21554
2022-07-27T00:34:42Z
media_statistika:ART
nmb a2200000Iu 4500
"191230 2019 eng "
2477-0647
1979-3693
dc
ANALISIS KEMISKINAN DI KABUPATEN MALUKU TENGGARA BARAT MENGGUNAKAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS)
Lembang, Ferry Kondo
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Pattimura
Patty, Henry Willyam Michel
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Pattimura
Maitimu, Feros
Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Pattimura
Poverty; MARS; Poor Households; GCV
Poverty is a condition where there is a condition where there is an inability of the community to meet basic needs such as food, clothing, shelter, education and health. MTB regency is one of the regions in Moluccas Province with a relatively high percentage of the poor population reaching 28.31%. The purpose of this study is to conduct poverty analysis in MTB using the MARS method. The problem of poverty is thought to be very much influenced by many factors, therefore the selection of the MARS method is considered very appropriate because it has the advantage of being able analyze high-dimensional data. The results showed the best MARS model was a combination BF=18, MI=3 and MO=0 with a minimum GCV value at 69.587. Variables that have a significant effect are the percentage RTM that do not have public toilet facilities (X5), the variable percentage of RTM that is the type of floor of a residential building made of poor quality soil / bamboo / wood (X4), and the percentage of RTM that does not own the building (X1).
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2019-12-30 14:53:04
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/21554
MEDIA STATISTIKA; Vol 12, No 2 (2019): Media Statistika
ind
Copyright (c) 2019 MEDIA STATISTIKA
oai:ojs.ejournal.undip.ac.id:article/4519
2016-03-15T17:06:56Z
media_statistika:ART
nmb a2200000Iu 4500
"120630 2012 eng "
2477-0647
1979-3693
dc
KAJIAN ESTIMASI-M IRLS MENGGUNAKAN FUNGSI PEMBOBOT HUBER DAN BISQUARE TUKEY PADA DATA KETAHANAN PANGAN DI JAWA TENGAH
Pradewi, Elen Dwi
Sudarno, Sudarno
Ordinary Least Squares (OLS) is one method of parameter estimation in regression analysis. However, the presence of outliers can cause estimation of regression coefficients obtained are not exact. Act of throwing away an outlier is not a wise move, because sometimes outliers provide significant information. Therefore, robust regression methods are needed to data contain outliers. This paper will use robust regression estimation method by M-estimation. This estimation use Iteratively Reweighted Least Squares (IRLS) method with weighting function by Huber and Tukey Bisquare. IRLS is applied to the case of food security in Central Java in 2007 that is influenced by the stock of rice, harvested area, average production, price of rice and the amount of consumption. The purpose of this writing is to compare goodness of M-estimation IRLS using Huber and Tukey Bisquare function in estimating the model parameters of food security in Central Java in 2007. Based on the research results can be concluded that the M-estimation by the Tukey Bisquare is better recommended than Huber function. This can be seen by value results of Mean Square Error and determination coefficient
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
2012-06-30 00:00:00
application/pdf
https://ejournal.undip.ac.id/index.php/media_statistika/article/view/4519
MEDIA STATISTIKA; Vol 5, No 1 (2012): Media Statistika
eng
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