The Geographically Weighted Regression (GWR) analysis is considered the most appropriate analysis to describe the Poverty model, including location. By employing the GWR analysis, this study is aimed to find out the proper model of poverty in West Java Province, which can describe the geographical characteristics of the location or district/city in West Java with the Kernel Bi-Square Weighting function. The secondary data were taken from 2018 which consist of the response variable of the Poor

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... rcentage (PP) and the independent variables which cover the Open Unemployment Rate (OUR), Human Development Index (HDI), Gross Regional Domestic Product (GRDP), Population Density Level (PDL), Regional Minimum Wage (RMW), Poor Population Percentage aged 15 years and high school (PPHS), and Literacy Rates (LR). These variables are estimated to influence the rate of poverty. Multiple regression (Global) and GWR regression (Local) were applied in the analysis, and the weighting function used was Bi-Square. Whereas the best model was selected using the criteria of the coefficient of determination R2 and the value of AIC. The results showed that the local GWR regression model has a coefficient of determination (R2) of 0.9253, meaning that the independent variables could explain 92.53% of the variation in the Poverty Percentage model. The remaining 7.47% is explained by other factors. Besides, the value of the global regression coefficient of determination is 0.7084. The AIC value for GWR is 352.437, and the AIC value for global regression is 363.227, meaning that the error value for GWR is smaller than the global regression. Thus, it can be concluded that the GWR (local) regression model is considered a better model. The variables that affect the percentage of poor people in the global regression model are the Open Unemployment Rate and the Regional Minimum Wage. Meanwhile, the variables that affect the percentage of poor people for 27 cities/districts of West Java vary.