DOI: https://doi.org/10.14710/medstat.14.1.89-97

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

*Dewi Retno Sari Saputro orcid scopus  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Sulistyaningsih Sulistyaningsih  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Purnami Widyaningsih  -  Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
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.
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Keywords: Additive noise; Ensemble, parameter estimation; SAR Model

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  1. Anselin, L. (2014). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers
  2. Anwar H, Qamar U, M. Q. A. (2014). Global Optimization Ensemble Model for Classification Methods. The Scientific World Journal
  3. Baba, N.M., Makhtar, M., Fadzli, S.A., dan Awang, M. . (2015). Current Issues in Ensemble Methods and Its Applications. Journal of Theoretical and Applied Information Technology, 81(2), 266–276
  4. Badan Pusat Statistik. (2004). Data dan Informasi Kemiskinan
  5. Behera, R.N., Roy, M., dan Dash, S. (2016). Ensemble based Hybrid Machine Learning Approach for Sentiment Classification-A Review. International Journal of Computer Applications., 146(6), 0975–8887
  6. Canuto, A. M. P., L. Oliveira, J.C.X. Junior, A. Santos, M. A. (2005). Performance and Diversity Evaluation in Hybrid and Non-hybrid Structures of Ensembles. Proceedings of the Fifth International Conference on Hybrid Intellegent Systems
  7. Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons
  8. De Bock, K. W., K. Coussement, and D. V. D. P. (2010). Ensemble Classification Based on Generalized Additive Models. Computational and Data Analysis, 54, 1535–1546
  9. Djuraidah, A. dan A. H. W. (2012). Regresi Spasial untuk Menentukan Faktor-faktor Kemiskinan di Provinsi Jawa Timur. Statistika, 12, 1–8
  10. Fadliana, A., Pramoedyo, H., & Fitriani, R. (2020). 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). Media Statistika, 13(2), 125–135. https://doi.org/10.14710/medstat.13.2.125-135
  11. Griffith, D. A. (2012). Advanced Spatial Statistics: Special Topics in The Exploration of Quantitative Spatial Data Series (Vol. 12). Springer Science & Business Media
  12. Kim, H.C., Pang, S., Je, H.M., Kim, D., and Bang, S. . (2003). Constructing Support Vector Machine Ensemble. Pattern Recognition, 36, 2757–2767
  13. Lembang, F. K., Patty, H. W. M., & Maitimu, F. (2019). Analisis Kemiskinan Di Kabupaten Maluku Tenggara Barat Menggunakan Pendekatan Multivariate Adaptive Regression Spline (Mars). Media Statistika, 12(2), 188. https://doi.org/10.14710/medstat.12.2.188-199
  14. LeSage. (1999). The Theory and Practice of Spatial Econometrics. University of Toledo
  15. Lu, X., Zhou, W., Ding, X, Shi, X., Luan, B. and Li, M. (2019). Ensemble Learning Regression for Estimating Unconfined Compressive Strength of Cemented Paste Backfill. IEEE, 7, 72125–72133
  16. Mevik, B.H., Segtnan, V.H. dan Naes, T. (2005). Ensemble Methods and Partial Least Squares Regression. Journal of Chemometrics, 18, 498–507
  17. Rohmawati, N., Wijayanto, H and Wigena, A. H. (2015). Ensemble Spatial Autoregressive Model on the Poverty Data in Java. Applied Statistical Sciences, 9, 2103–2110
  18. Saifudin, A. dan Wahono, R. S. (2016). Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering, 1(1)
  19. Saputro, D. R. S., Sukmayanti, A., & Widyaningsih, P. (2019). The Nonparametric Regression Model Using Fourier Series Approximation and Penalized Least Squares (PLS) (Case on Data Proverty in East Java). Journal of Physics: Conference Series, 1188(1). https://doi.org/10.1088/1742-6596/1188/1/012019
  20. Sinta, D., Wijayanto, H. and Sartono, B. (2014). Ensemble K-Nearest Neighbor Method to Predict Rice Price in Indonesia. Applied Statistical Sciences, 8, 7993–8005
  21. Vrrag M, N. T. (2014). The Application of Ensemble Methods in Forecasting Bankruptcy. Financial and Economic Review, 13(4), 178–193
  22. Wu Z, H. N. (2005). Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method, Kernels and Ensemble. Perspective on Statistical Learning
  23. Zhu, M. (2012). Kernel and Ensemble: Perspective on Statistical Learning. In The American Statistician. (Vol. 62, Issue 2, pp. 97–109)

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