<![CDATA[Abstract. Poverty in West Java Province in March 2024 was recorded at 7.46 percent, or 3.89 million people, with patterns that vary across regions due to spatial effects. This condition indicates that classical regression, which assumes constant parameters across all regions, is less appropriate to use, making it necessary to apply Geographically Weighted Regression (GWR), a method capable of accommodating local variations in relationships between regions. This study aims to model the number of poor population across 27 regencies/cities in West Java Province in 2024 using GWR, with independent variables including the Gini ratio, GRDP growth rate, per capita expenditure, the Community Literacy Development Index (IPLM), minimum wage, and the Labor Force Participation Rate (TPAK). The spatial weighting used an adaptive bisquare kernel function, with the optimum bandwidth determined through the Cross Validation method, and data processing was carried out using R Studio. The model fit test results show an F-value (2.3704) greater than the F-table value (2.235) with a p-value of 0.03764, indicating that GWR is more appropriate than OLS. The GWR model produced different equations for each region, with an AICc value of 33.7708, and significant variables that varied across regions, grouped into six clusters of regencies/cities. Abstrak. Kemiskinan di Provinsi Jawa Barat pada Maret 2024 tercatat sebesar 7,46 persen atau 3,89 juta jiwa, dengan pola yang bervariasi antarwilayah akibat adanya pengaruh spasial. Kondisi ini menunjukkan bahwa regresi klasik yang mengasumsikan parameter konstan di seluruh wilayah kurang tepat digunakan, sehingga diperlukan metode Geographically Weighted Regression (GWR) yang mampu mengakomodasi variasi hubungan antarwilayah secara lokal. Penelitian ini bertujuan memodelkan jumlah penduduk miskin di 27 kabupaten/kota Provinsi Jawa Barat tahun 2024 menggunakan GWR, dengan variabel independen meliputi rasio gini, laju pertumbuhan PDRB, pengeluaran per kapita, IPLM, upah minimum, dan TPAK. Pembobot spasial menggunakan kernel adaptive bisquare dengan bandwidth optimum ditentukan melalui metode Cross Validation, dan pengolahan data dilakukan menggunakan R Studio. Hasil uji kesesuaian model menunjukkan F-hitung (2,3704) > F-tabel (2,235) dengan p-value 0,03764, sehingga GWR lebih sesuai digunakan dibandingkan OLS. Model GWR menghasilkan persamaan berbeda untuk tiap wilayah dengan nilai AICc sebesar 33,7708, serta variabel signifikan yang bervariasi antarwilayah, dikelompokkan menjadi enam kelompok kabupaten/kota.]]>
