Kernel Performance in Geographically Weighted Regression Model to Determine Factors Affecting Human Development Index in South Sulawesi Province - Angelia Fransisca Adatunaung; Djoni Hatidja; Winsy Christo Deilan Weku (Sam Ratulangi University, 2023)
The aims of this study was determine at kernel performance by selecting the best model from three different types of kernels and determining the factors that influence the Human Development Index in South Sulawesi Province using the Geographically Weighted Regression (GWR) model This study uses secondary data from the Central Bureau of Statistics of South Sulawesi Province with independent variables namely human development index (HDI, Y) and the dependent variable namely life expectancy (UHH) (X1), per capita expenditures (X2) and gross regional domestic product (GRDP) (X3) and the longitude and latitude values ​​obtained from the google maps application. The methods carried out in this study are the GWR method and the kernels used are gaussian kernels, bisquare kernels and tricube kernels. The results of this study show that the best model that can be used is the GWR model with a tricube kernel with AIC values ​​= 81.5543700 and R2 = 90.67 percent. GWR Model with kernel tricube is able to determine the factors that influence the human development index in South Sulawesi in 2022. Keywords: Geographically Weighted Regression; human development index; tricube kernel