<![CDATA[This study aims to model the open unemployment rate (OUR) in regencies and cities in Java using a spatial regression approach. The analysis uses secondary data from 2024 covering 118 regencies and cities in Java. The response variable is the open unemployment rate, while the predictor variables include the regency/city minimum wage, average years of schooling, labor force participation rate, GRDP growth rate, and population growth rate. The analysis begins with multiple linear regression using the OLS method followed by classical assumption tests. Spatial dependence is examined using Moran’s I with several spatial weight matrices, namely queen contiguity, inverse distance, and k-nearest neighbors. The Lagrange Multiplier test is then conducted to determine the appropriate spatial regression model for each spatial weight matrix. The results show that the spatial autoregressive model (SAR) with the queen contiguity spatial weight matrix is the best model. The selected model produces a coefficient of determination of 69,37% and an AIC value of 352,46. The estimation results indicate that labor force participation rate, and population growth rate significantly affect the open unemployment rate in Java in 2024, while the spatial parameter confirms spatial dependence among regions.]]>
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