(untitled) (Jurnal EurekaMatika, 2025)
Poverty is influenced by diverse regional conditions, requiring an approach that captures spatial variation. Robust Geographically Weighted Regression with M-estimation (RGWR-M) and Tukey Bisquare weighting was applied to analyze the poverty severity index in districts and cities of West Java Province in 2023. This method was chosen because Ordinary Least Squares (OLS) is prone to heterogeneity and Geographically Weighted Regression (GWR) is sensitive to outliers. Independent variables include the percentage of poor population, access to safe drinking water, population density, and per capita expenditure. Results show that RGWR-M achieved the best performance with a Mean Absolute Deviation (MAD) of 0.0443, and all variables had significant effects. These findings highlight the importance of robust models in spatial analysis to provide accurate estimates and support targeted poverty alleviation policies. Keywords: Poverty, Robust regression, RGWR-M, Spasial heterogeneity, Tukey bisquare Kemiskinan dipengaruhi oleh kondisi yang berbeda di setiap wilayah sehingga memerlukan pendekatan yang mampu menangkap variasi spasial. Robust Geographically Weighted Regression dengan pendugaan-M (RGWR-M) dan pembobot Tukey Bisquare digunakan untuk menganalisis indeks keparahan kemiskinan di kabupaten/kota Provinsi Jawa Barat tahun 2023. Metode ini dipilih karena OLS rentan heterogenitas dan GWR sensitif terhadap outlier. Variabel independen yang diuji adalah persentase penduduk miskin, akses air minum layak, kepadatan penduduk, dan pengeluaran per kapita. Hasil penelitian menunjukkan RGWR-M memberikan kinerja terbaik dengan MAD 0,0443, dan semua variabel berpengaruh signifikan. Temuan ini menegaskan pentingnya model robust dalam perumusan kebijakan pengentasan kemiskinan.