Pemodelan Spatial Autoregressive (SAR) untuk Persentase Kemiskinan di Jawab Barat Tahun 2021 - Muhammad Saifudin Nur; Prizka Rismawati Arum; Fenny Amalia Adani; Cintadea Amanda Dwi Aryani; Aqsal Maulana (Universitas Islam Negeri Alauddin Makassar, 2024)
Spatial Autoregressive Model (SAR) is a spatial regression model that has a spatial effect on the dependent variable. Spatial data refers to data that contains geographic information or regional location. Spatial analysis process consisting of visualization, exploration and modeling. This study uses the response variable (y), namely poverty, and 5 predictor variables, namely AMH (x1), open response rate (x2), GRDP (x3), participation rate (APS) (x4), and life expectancy (x5). A significant factor influencing West Java's poverty is GRDP (x3). The best model for the data in this study is the SAR because the R square value in the spatial regression is greater than the classical regression of 62%. There is no significant independent variable in the classical regression model but after modeling using SAR there is one significant variable which means it gives added value to the SAR regression model as the best model.