Implementation of the Semiparametric Geographically Weighted Logistic Regression (GWLRS) Model for Predicting Poverty Depth Index in Central Java Province
DOI:
https://doi.org/10.52435/jaiit.v8i1.767Keywords:
Central Java, GWLRS, Poverty, Spatial Analysis, Spatial RegressionAbstract
Poverty in Central Java Province remains a significant multidimensional issue, characterized by socio-economic disparities across regencies and municipalities and high rates of school dropouts among children. This study aims to evaluate the influence of socio-economic variables on poverty depth (P1 Index) at both local and global levels. The approach employed is the Geographically Weighted Logistic Regression Semiparametric (GWLRS), which integrates local and global effects. The model uses two types of spatial weights: Adaptive Gaussian Kernel and Queen Contiguity, with predictor variables including Dependency Ratio, Minimum Regional Wage (UMK), Number of Industries, Open Unemployment Rate (TPT), Adequate Housing, and Sanitation. Parameter estimation was conducted using Maximum Likelihood. The results indicate that the Dependency Ratio, Adequate Housing, and Sanitation are locally significant in 2–5 regions. Local coefficients for the Dependency Ratio range from 0.42 to 11.57 (mean 3.33), Adequate Housing from -15.808 to -2.371 (mean
-5.95), and Sanitation from 1.86 to 17.27 (mean 5.71). The model correctly predicts 31 out of 35 cases, yielding an accuracy of 91.4%. The Number of Industries, UMK, and TPT are not globally significant, indicating that their effects are more stable across regions. In conclusion, the GWLRS model effectively captures the spatial heterogeneity of poverty determinants and provides quantitative insights that can support more targeted, location-based poverty alleviation policies in Central Java Province.
References
Siti Hartina Daulay and Elmanani Simamora, “PEMODELAN FAKTOR-FAKTOR PENYEBAB KEMISKINAN DI PROVINSI SUMATERA UTARA MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR),” J. Ris. RUMPUN Mat. DAN ILMU
Pengetah. ALAM, vol. 2, no. 1, pp. 47–60, Jan. 2023, doi: 10.55606/jurrimipa.v2i1.646.
Y. Mansur, “Analisis Perkembangan Penduduk Miskin, Karakteristik Kemiskinan dan Kedalaman Kemiskinan di Indonesia,” J. EMT KITA, vol. 8, no. 1, pp. 18–31, Jan. 2024, doi: 10.35870/emt.v8i1.1930.
“Profil Kemiskinan di Indonesia September 2024 Provinsi Jawa Tengah,” Badan Pusat Statistik Jawa Tengah. [Online]. Available: https://jateng.bps.go.id/id/pressrelease/2025/01/15/1547/profil-kemiskinan-di-indonesia-september-2024-provinsi-jawa-tengah.html
F. A. Guampe, A. S. Walenta, and F. B. Kawani, “Pengaruh Pertumbuhan Ekonomi Dan Pengangguran Terbuka Terhadap Kemiskinan Di Indonesia Tahun 2001-2021,” JPEK J. Pendidik. Ekon. Dan Kewirausahaan, vol. 6, no. 1, pp. 92–102, Jun. 2022, doi: 10.29408/jpek.v6i1.5536.
A. I. Nurfarizki, B. Setiawan, F. R. Nugroho, W. Rizky, and M. R. W. Azka, “Pengaruh Indeks Pembangunan Manusia, Pertumbuhan Ekonomi dan Lama Sekolah terhadap Kemiskinan di Provinsi Jawa Tengah,” 2024.
K. Sari’ah, “Pengaruh Kemiskinan dan Pertumbuhan Ekonomi Terhadap Indeks Pembangunan Manusia di Provinsi Jawa Tengah dalam Prespektif Ekonomi Islam Ibnu Khaldun Tahun 2019-2023,” J. Manag., vol. 3, 2024.
A. Anwar, “SPATIAL ANALYSIS OF REGIONAL POVERTY IN CENTRAL JAVA
INDONESIA,” J. Din. Ekon. Pembang., vol. 5, no. 1, pp. 36–55, Jul. 2022, doi: 10.14710/jdep.5.1.36-55.
T. Saifudin, L. S. Panjaitan, S. Falasifah, and Yan Dwi, “Application of Geographically Weighted Logistic Regression in Modeling the Human Development Index in East Java,” J. Apl. Stat. Komputasi Stat., vol. 16, no. 1, pp. 43–57, Jun. 2024, doi: 10.34123/jurnalasks.v16i1.587.
F. Fitriatusakiah, A. K. Jaya, and L. P. Talangko, “Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017,” ESTIMASI J. Stat. Its Appl., pp. 105–114, Jul. 2021, doi: 10.20956/ejsa.v2i2.11309.
W. Kang and T. M. Oshan, “Scale and correlation in multiscale geographically weighted regression (MGWR),” J. Geogr. Syst., vol. 27, no. 3, pp. 399–424, Jul. 2025, doi: 10.1007/s10109-025-00468-1.
A. H. Azizah, N. Nurjannah, A. A. R. Fernandes, and R. Hamdan, “GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM,” MEDIA Stat., vol. 16, no. 1, pp. 47–58, Nov. 2023, doi: 10.14710/medstat.16.1.47-58.
N. Nurhasanah, W. Widiarti, D. E. Nurvazly, and M. Usman, “Penerapan Model Geographically Weighted Logistic Regression dengan Fungsi Pembobot Adaptive Gaussian Kernel pada Data Kemiskinan,” Jambura J. Math., vol. 6, no. 2, pp. 204–211, Aug. 2024, doi: 10.37905/jjom.v6i2.26504.
M. Irwansyah, R. Ruliana, and M. K. Aidid, “Analisis Regresi Balanced Panel dengan Komponen Galat Dua Arah pada Kasus Melek Huruf Masyarakat di Provinsi NTB,” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 3, no. 1, p. 10, Mar. 2021, doi: 10.35580/variansiunm14644.
A. Kalnins and K. Praitis Hill, “The VIF Score. What is it Good For? Absolutely Nothing,” Organ. Res. Methods, vol. 28, no. 1, pp. 58–75, Jan. 2025, doi: 10.1177/10944281231216381.
H. Yasin, B. Warsito, and A. R. Hakim, REGRESI SPASIAL (Aplikasi dengan R). in 1. WADE Group, 2020.
S.-L. Shen, J.-L. Cui, and X.-Q. Wu, “A simple test for spatial heteroscedasticity in spatially varying coefficient models,” J. Stat. Comput. Simul., vol. 91, no. 8, pp. 1580–1592, May 2021, doi: 10.1080/00949655.2020.1862112.
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