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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI BIMASTER Indonesian Journal of Physics and Nuclear Applications Jurnal Statistika dan Aplikasinya Tensor: Pure and Applied Mathematics Journal
Naomi Nessyana Debataraja
Department Of Statistics, Tanjungpura University
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Physics Social Sciences Other

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Journal : Jurnal Statistika dan Aplikasinya

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Application of Geographically Weighted Regression for Modeling the Poverty Cases in Kalimantan, Indonesia Noerul Hanin; Irvan Meilandra; Naomi Nessyana Debataraja; Retno Pertiwi
Jurnal Statistika dan Aplikasinya Vol 8 No 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08101

Abstract

Poverty is one of the most global issues that remains a concern worldwide, including in Indonesia. Indonesia is among the top 100 poorest countries in the world, ranked 73rd, to be exact. Government wants to decrease the national poverty rate, as outlined in the 2021-2024 National Medium-Term Development Plan, with the expected percentage of poor people in Indonesia on 2024 being 6.5 to 7 percent. Unfortunately, the hope for a reduction in the poverty rate has not been achieved in several regions, such as in 4 out of 5 provinces in Kalimantan. Therefore, the analyzing factors causing poverty in the Kalimantan region is conducted using the Geographically Weighted Regression method in order to give clearly information for government to decrease the poor rate in this region. GWR (Geographically Weighted Regression) is an extension of the regression method. The equation parameters for each observation location differ from one location to another. The weighting function used were fixed gaussian, fixed bisquare, fixed tricube, adaptive gaussian, adaptive bisquare, and adaptive tricube. Based on R2 and AIC value, the best model is the model with fixed tricube function. The R2 score is 0.8952, while the AIC score is 155.83. The GWR model is better than OLS or global regression model. Thus, spatial analysis to see the factors affecting the percentage of poor people in each regency and city in Kalimantan, Indonesia has been successfully carried out.
Co-Authors Beatrice Beatrice Chelse Dwi Apriyana Tamba Dadan Kusnandar Irvan Meilandra Junita Junita Mutiara Cindy Nur Fitria Noerul Hanin NURFITRI IMRO’AH Puji Sri Lestari Rahmat Khairul Saleh Retno Pertiwi Rossie Wiedya Nusantara Setyo Wira Rizki Shantika Martha Shantika Marthal