Modeling Human Development Index of East Java Using Spatial Autoregressive and Spatial Error Ensemble

Nadia Aulia Jelita, Program Studi Statistika, Universitas Sebelas Maret, Surakarta, Indonesia
Sri Sulistijowati Handajani, Program Studi Statistika, Universitas Sebelas Maret, Surakarta, Indonesia
Irwan Susanto, Program Studi Statistika, Universitas Sebelas Maret, Surakarta, Indonesia

Abstract


The human development index (HDI) is an indicator used to monitor the government's success in developing the quality of human life. East Java Province's HDI is the lowest compared to other provinces on Java Island. Therefore, it is necessary to improve human development in this province. Attention must be paid to all aspects of human development, including the relationship between neighboring regions. The spatial regression method is an analysis method that considers the spatial dependency of the data. Ensemble spatial regression combines several spatial models by adding noise to the response variable, which is expected to reduce the diversity in the data. This research aims to use ensemble spatial regression to examine the East Java HDI. East Java HDI has spatial lag and spatial error dependence, modeled with SAR and SEM. Queen contiguity is used as a spatial weight. The SEM model does not fulfill the homogeneity assumption, so it is continued with the ensemble method. The ensemble method is proven to reduce diversity, so  SEM Ensemble fulfills the assumption of homoscedasticity. After analysis using SAR and SEM Ensemble, the SAR model was chosen as the best model with the largest  and lowest AIC value. Significant variables on East Java HDI are life expectancy, expected years of schooling, average years of schooling, and expenditure per capita.

Keywords


HDI; spatial regression; ensemble method; homogeneity

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References


Anselin, L. (1988). Spatial Econometrics: Methods and Models (Vol. 4). Springer Netherlands. https://doi.org/10.1007/978-94-015-7799-1

Badan Pusat Statistik. (2022). Berita Resmi Statistik Indeks Pembangunan Manusia (IPM) Tahun 2022. Badan Pusat Statistik. https://www.bps.go.id/id/pressrelease/2022/11/15/1931/indeks-pembangunan-manusia--ipm--indonesia-tahun-2022-mencapai-72-91--meningkat-0-62-poin--0-86-persen--dibandingkan-tahun-sebelumnya--72-29-.html

Badan Pusat Statistik Provinsi Jawa Timur. (2022). Berita Resmi Statsitsik Indeks Pembangunan Manusia (IPM) Jawa Timur Tahun 2022. Badan Pusat Statistik Provinsi Jawa Timur. https://jatim.bps.go.id/pressrelease/2022/11/15/1309/indeks--pembangunan-manusia--ipm--jawa-timur-pada-tahun-2022--mencapai-72-75.html

De Bock, K. W., Coussement, K., & Van den Poel, D. (2010). Ensemble classification based on generalized additive models. Computational Statistics & Data Analysis, 54(6), 1535–1546. https://doi.org/10.1016/j.csda.2009.12.013

Draper, N., & Smith, H. (1992). Analisis Regresi Terapan : Edisi Kedua (Edisi Kedua). Gramedia Pustaka Utama.

Dubin, R. (2009). Spatial Weights. In The SAGE Handbook of Spatial Analysis (pp. 125–157). SAGE Publications, Ltd. https://doi.org/10.4135/9780857020130.n8

Gujarati, D. N. (2004). Basic Econometrics (4th ed.). The McGraw-Hill Companies. http://13.235.221.237:8080/jspui/bitstream/123456789/443/1/Basic-Econometrics---Gujaratipdf.pdf

Handajani, S. S., Savita, C. A., Pratiwi, H., & Susanti, Y. (2018). Best weighted selection in handling error heterogeneity problem on spatial regression model. Proceedings of the International Conference on Mathematics and Islam, 293–299. https://doi.org/10.5220/0008521002930299

Hidayah, N. R., & Indrasetianingsih, A. (2019). Analisis regresi spatial durbin model (SDM) untuk pemodelan kemiskinan Provinsi Jawa Timur tahun 2017. J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 12(1), 40–46. https://doi.org/10.36456/jstat.vol12.no1.a1994

Novitasari, D., & Khikmah, L. (2019). Penerapan model regresi spasial pada indeks pembangunan manusia (IPM) di Jawa Tengah. Jurnal Statistika, 19(2), 123–134. https://doi.org/10.29313/jstat.v19i2.5068

Purba, D. S., Tarigan, W. J., Sinaga, M., & Tarigan, V. (2021). Pelatihan penggunaan software spss dalam pengolahan regressi linear berganda untuk mahasiswa fakultas ekonomi Universitas Simalungun di masa pandemi covid 19. Karya Abadi, 5(2), 202–208. https://doi.org/10.22437/jkam.v5i2.15257

Santoso, E., Jumiati, A., Hadi Priyono, T., & Putomo Somaji, R. (2022). Determinan indeks pembangunan manusia di Provinsi Jawa Timur: model crossectional spasial. JAE (JURNAL AKUNTANSI DAN EKONOMI), 7(1), 103–112. https://doi.org/10.29407/jae.v7i1.17884

Savita, C. A., Handajani, S. S., & Winarno, B. (2017). Penerapan model regresi ensemble non-hybrid pada data kemiskinan di Provinsi Jawa Tengah. The 6th University Research Colloquium 2017, 127–134. https://journal.unimma.ac.id/index.php/urecol/article/view/1237

Sazaen, E. A., Wasono, R., & Nur, I. M. (2020). Non-hybrid ensemble spatial regression on human development index (IPM) in Central Java. Jurnal Litbang Edusaintech, 1(1), 23–34. https://doi.org/10.51402/jle.v1i1.4

Si’lang, I. L. S., Hasid, Z., & Priyagus. (2019). Analisis faktor-faktor yang berpengaruh terhadap indeks pembangunan manusia. Jurnal Manajemen, 11(2), 159–169. http://journal.feb.unmul.ac.id/index.php/JURNALMANAJEMEN

Viton, P. A. (2010). Notes on Spatial Econometric Models. The Ohio State University. https://www.yumpu.com/en/document/read/3858779/notes-on-spatial-econometric-models-the-ohio-state-university

Ward, M., & Gleditsch, K. (2008). Spatial Regression Models. SAGE Publications, Inc. https://doi.org/10.4135/9781412985888

Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 01(01), 1–41. https://doi.org/10.1142/S1793536909000047




DOI: https://doi.org/10.21831/pythagoras.v19i2.78621

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