Pemodelan Geographically Weighted Regression pada Tingkat Pengangguran Terbuka di Pulau Jawa Tahun 2020 - Septiyana, Alya Nur; Fatkhurrohman, Ikbal; Fikri, Fajriana Fadhlul; S, Riabela; Prananggalih, Ahmad Tegar; Bachtiar, Aji Bagus; ML, Dhitasya Salsabila; Berliana, Sarni Maniar (Politeknik Statistika STIS, 2023)
Geographically Weighted Regression (GWR) is a regression model that takes into account spatial effects in modeling the relationship between the response variable and the independent variable due to spatial heterogeneity in the data studied. The unemployment rate by regency/municipality in Java Island shows spatial heterogeneity so that the GWR modeling appropriate to be applied in determining the factors that influence the unemployment rate. The results show that the number of residents, the number of workers in the agricultural sector, the regional minimum wage, the mean years of schooling, and the labor force participation rate have different effects for different locations, while domestic investment has no significant effect on the unemployment rate either globally as well as locally. The application of the GWR model is better than the ordinary regression model based on the Akaike information criterion.