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This study aims to identify the factors Unemployment Rate (UR) in West Java and develop the appropriate model. This study applied the location (spatial) element using Geographically Weighted Regression (GWR). The GWR model was compared with the global regression. The data used in this study are secondary data on 2017 UR for 27 cities/ regencies in West Java. The dependent variable (Y) is the Unemployment Rate (UR), the independent variables include Population Density Level (PDL), Gross Regional Domestic Product(GRDP), Regional Minimum Wage (RMW), Level of Active Labor Participation Rate (ALPR), and Human Development Index (HDI). The results show that the GWR model provides a coefficient of determination (R) more significant than the global regression model. The Akaike Information Criteria (AIC) value of the GWR model is smaller than the global regression model, meaning that the local regression model of error value is smaller than the global regression model. In other words, the local regression model is better than the global regression model. The factor affecting UR globally is RMW. There are 27 different combinations of local regression models according to the number of cities/regencies in West Java.

Comparison of Geographically Weighted Regression Analysis and Global Regression on Modeling the Unemployment Rate in West Java