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PERBANDINGAN MODEL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN FUNGSI PEMBOBOT FIXED BISQUARE KERNEL DAN FUNGSI PEMBOBOT ADAPTIVE BISQUARE KERNEL (STUDI KASUS: FAKTOR-FAKTOR YANG MEMENGARUHI TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA BARAT MENURUT KABUPATEN/KOTA TAHUN 2024)
The Open Unemployment Rate (OUR) is one of the important indicators that show the success of development in a region. West Java Province has diverse regional characteristics, resulting in varying unemployment patterns between districts/cities. Analysis using a global regression model with the Ordinary Least Square (OLS) parameter estimation method is often inaccurate because it assumes that the model parameters are constant for all observation locations, thus failing to describe the diversity of characteristics between regions or spatial heterogeneity, which is a condition where the same independent variable has a different effect on different regions or locations. As a solution, the Geographically Weighted Regression (GWR) method is used, which allows parameter estimation to be performed locally at each observation point, so that each region has different regression parameter values according to its spatial characteristics. This study aims to determine the factors and the best model that influence the Open Unemployment Rate (OUR) in West Java Province in 2024 based on the Geographically Weighted Regression (GWR) method using the Fixed Bisquare Kernel and Adaptive Bisquare Kernel weighting functions, involving the Labor Force Participation Rate (TPAK), the Percentage of Poor Population, Population Growth, Gross Regional Domestic Product (GRDP), and Labor Absorption as independent variables. The best model was obtained with GWR using the Adaptive Bisquare Kernel weighting function with a coefficient of determination (R2) value of 82,92% and an Akaike Information Criterion (AIC) value of 68,3631.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
PERBANDINGAN MODEL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN FUNGSI PEMBOBOT FIXED BISQUARE KERNEL DAN FUNGSI PEMBOBOT ADAPTIVE BISQUARE KERNEL (STUDI KASUS: FAKTOR-FAKTOR YANG MEMENGARUHI TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA BARAT MENURUT KABUPATEN/KOTA TAHUN 2024) (Text)
PERBANDINGAN MODEL GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN FUNGSI PEMBOBOT FIXED BISQUARE KERNEL DAN FUNGSI PEMBOBOT ADAPTIVE BISQUARE KERNEL (STUDI KASUS: FAKTOR-FAKTOR YANG MEMENGARUHI TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA BARAT MENURUT KABUPATEN/KOTA TAHUN 2024) (Other)
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