DOI: https://doi.org/10.21456/vol9iss2pp192-203

Sistem Peramalan Kebutuhan Hidup Layak Minimum (Kapita/Bulan) Kota Bandung

Rifqi Fahrudin  -  Universitas Komputer Indonesia, Indonesia
*Irfan Dwiguna Sumitra orcid scopus  -  Universitas Komputer Indonesia, Indonesia
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract

The importance of inflation forecasting is used as a reference for estimating the Need for Living (KHL). If inflation can be predicted with high accuracy, it can certainly be used as the basis for government policy making in anticipating future economic activity. This study aims to produce a model of inflation data forecasting system, the forecasting results can be used as a reference for determining the Decent Living Needs (KHL) of a single worker in one month. The data used in forecasting is inflation data for January 2011 - December 2017 while the KHL data for the food and beverage category is obtained from the food price portal. The method used in this study is the SARIMA-SES hybrid method. In forecasting the inflation rate where the data is in the form of time series, the SARIMA-SES hybrid method can show more accurate forecasting results than using a single method. Based on the comparison of the overall forecasting model and by combining the SARIMA (1,0,1) (1,0,1) 12 and SES models with alpha 0,6 the smallest error value with MAD value 0,114, MSE 0,017 and 0,39% for MAPE. From these results, it was gathered that inflation forecasting in Bandung City using the SARIMA-SES hybrid method has a high accuracy value so that the results of the KHL value calculation with the forecasting inflation value approach the actual value. From these values can be used as a reference for the decision making of a single worker in fulfilling their life needs one month.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
COPYRIGHT TRANSFER AGREEMENT
Subject
Type Research Instrument
  Download (406KB)    Indexing metadata
Keywords: Inflation; Forecasting; SARIMA; SES; KHL
Funding: Yeffry Handoko Putra; Tias Syahroni; Universitas Komputer Indonesia; Hibah Tesis Magister Kemenristek Dikti No. 2898 / L4 / PP / 2019

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi Analisis Perancangan Sistem Penerimaan Siswa Baru Menggunakan Framework Zachman Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine More recent articles
Most cited articles
Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro Data Clustering Menggunakan Metodologi CRISP-DM Untuk Pengenalan Pola Proporsi Pelaksanaan Tridharma Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia Sistem Informasi Pengukuran Kinerja Pada Perkebunan Kelapa Sawit Dengan Menggunakan Metode Balanced Scorecard More cited articles
  1. Biri, R., Langi, Y.A., & Paendong, M.S., 2013. Penggunaan Metode Smoothing Eksponensial dalam Meramal Pergerakan Inflasi Kota Palu. Jurnal Ilmiah Sains, 13(1), 68-73
  2. Bowerman, B.L. and O`Connell, R.T., 2018. Forecasting and time series : an applied approach / Bruce L. Bowerman, Richard T. O`Connell
  3. Peraturan Menteri Tenaga Keja dan Transmigrasi RI No. 13 Tahun 2012 tentang Komponen dan Pelaksanaan Tahapan Pencapaian Kebutuhan Hidup layak
  4. Peraturan Menteri Tenaga Keja dan Transmigrasi RI No. 17 Tahun 2005 tentang Komponen dan Pelaksanaan Tahapan Pencapaian Kebutuhan Hidup layak
  5. Peraturan Pemerintah Nomor 78 Tahun 2015 tentang Pengupahan
  6. Pongdatu, G.A.N. & Putra, Y.H., 2018. Seasonal Time Series Forecasting using SARIMA and Holt Winter’s Exponential Smoothing. In IOP Conference Series: Materials Science and Engineering (Vol. 407, No. 1, p. 012153). IOP Publishing
  7. Sofyan, A., 1984. Teknik dan metode peramalan, Jakarta Penerbit Fak.Ekonomi Universitas Indonesia
  8. Suseno, S.A., 2009. Inflasi. Jakarta: Pusat Pendidikan dan Studi Kebanksentralan (PPSK) BI
  9. Swandayani, D.M. & Kusumaningtias, R., 2012. Pengaruh Inflasi, Suku Bunga, Nilai Tukar Valas dan Jumlah Uang Beredar terhadap Profitabilitas pada Perbankan Syariah di Indonesia Periode 2005-2009. AKRUAL: Jurnal Akuntansi, 3(2), 147-166
  10. Undang Undang Nomor 13 Tahun 2003 tentang Ketenagakerjaan
  11. Wei, W.W.S., 2006. Time Series Analysis Univariate and Multivariate Methods SECOND EDITION
  12. Wiyanti, T., Dian & Pulungan, Reza, 2012. Peramalan Deret Waktu Menggunakan Model Fungsi Basis Radial (RBF) dan Auto Regressive Integrated Moving Average (ARIMA). 35. 175-182
  13. Zhang, G., 2003. Time series forecasting using a hybrid ARIMA and neural network model. J.Neurocomputing 50:159-175

Last update:

  1. Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

    Didin Muhjidin, Tedjo Sukmono. Procedia of Engineering and Life Science, 1 (2), 2021. doi: 10.21070/pels.v1i2.1002

Last update: 2025-06-14 14:04:13

No citation recorded.