Pemodelan Geographically Weighted Regression Menggunakan Pembobot Kernel Fixed dan Adaptive pada Kasus Tingkat Pengangguran Terbuka di Indonesia

Abstract

Unemployment Rate (UR) is an indicator for measuring the unemployment. Increase in the number of TPT in Indonesia by 1.84%, this is due to the impact of the covid-19 pandemic. analysis to find out the factors that affect TPT in Indonesia is by using multiple linear regression. The results showed that the data contained heterokedasticity and spatial aspects. Spatial data analysis continued with the point approach is by the Geographically Weighted Regression method (GWR). GWR is a weighted regression that results in a model that is local. GWR modeling uses weighting kernels Fixed Gaussian, Adaptive Gaussian , Fixed Bi-Square, and Adaptive Bi-Square produces that GWR Adaptive Bi-Square better, review value of the R2,AIC and JKG. The ability of the GWR model explains the effect of UR on factors (Labor Force or economically active, Health Complaint and Poverty Percentage) by 89.1%.