PERBANDINGAN MODEL MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN FUNGSI PEMBOBOT FIXED DAN ADAPTIVE BISQUARE KERNEL

Alfiyah Nurjannah, NIM.: 20106010012 (2024) PERBANDINGAN MODEL MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN FUNGSI PEMBOBOT FIXED DAN ADAPTIVE BISQUARE KERNEL. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The employment size in Indonesia, particularly in Central Java Province, exhibits significant disparities between job opportunities with population and workforce, resulting in spatial heterogeneity. Spatial heterogeneity occurs because the same independent variables can have different impacts at different observation locations. A method that can be used to modeling data with spatial heterogeneity is MGWR MGWR combines global linear regression with local GWR. This research aims to identify the factors and the best model affecting employment size in Central Java Province in 2022 using the MGWR method employing fixed and adaptive bisquare kernel weighting functions involving UMK, IPM, population size, PDRB, and unemployment rate as independent variables. The optimal model was obtained with MGWR using the adaptive bisquare kernel weighting function with an

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