Geographically Weighted Regression (GWR) Modeling with Adaptive and Fixed Kernel Functions on Stunting Event in South Sulawesi Province - Serlinda (Universitas Brawijaya, 2026)
The issue of stunting is currently a national priority in Indonesia. South Sulawesi Province has a fairly high prevalence of stunting and is included in the top 10 provinces with the highest stunting rates in Indonesia. So research is needed to determine the variables that influence the prevalence of stunting to support prevention efforts. This research discusses Geographically Weighted Regression (GWR) modeling using fixed kernel and adaptive kernel weighting functions to analyze variables that influence stunting cases in South Sulawesi Province. The independent variables used are the percentage of low birth weight (LBW), children who do not have IMD, adequate sanitation, children who do not have MCH books, and poor people, while the dependent variable is the prevalence of stunting. The data used is secondary data for 2023 sourced from publications by the Central Statistics Agency (BPS). The GWR method is applied to capture the influence of spatial heterogeneity between regions using six weighting functions: Fixed Gaussian Kernel, Fixed Bisquare Kernel, Fixed Tricube Kernel, Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. The selection of the best model is carried out based on the Akaike Information Criterion (AIC). The research results show that the Adaptive Bisquare Kernel weighting function provides the best results with the smallest AIC value. The variables that have a significant effect on the prevalence of stunting are the percentage of children without IMD, adequate sanitation and poor people.