Penerapan Model Geographically Weighted Logistic Regression dengan Fungsi Pembobot Adaptive Gaussian Kernel pada Data Kemiskinan

Nunung Nurhasanah, Widiarti Widiarti, Dina Eka Nurvazly, Mustofa Usman

Abstract


Regression analysis is one statistical method used to determine the relationship between a dependent variable and one or more independent variables. Dependent variables that are categorical are analyzed using logistic regression analysis. Geographically Weighted Logistic Regression (GWLR) is a method that is a local version of logistic regression, where location factors are considered. This method assumes that the dependent variable data are distributed binomially. In this study, the GWLR method is used to determine the factors influencing the poverty percentage in West Java Province in 2022 using an adaptive Gaussian kernel weighting function. The variables used are per capita expenditure, average length of schooling, Gross Regional Domestic Product (GRDP) per capita, and population density. The results of this study indicate that the variables of per capita expenditure, Gross Regional Domestic Product (GRDP) per capita, and population density significantly influence the poverty percentage in West Java Province in 2022.

Keywords


GWLR; Adaptive Gaussian Kernel; Poverty

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DOI: https://doi.org/10.37905/jjom.v6i2.26504

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