Geographically weighted logistic regression modeling on stunting cases in Indonesia

Abstract

Stunting is a condition of failure to thrive in children as a result of chronic malnutrition, so the child is too short at his/her age. Stunting harms children’s growth and affects the quality of human resources in the future. To reduce the prevalence of stunting in Indonesia, the government determined priority areas for handling stunting cases in Indonesia. This study aims to determine the variables that affect the status of priority areas for handling stunting in Indonesia. The model used in this study is Geographically Weighted Logistic Regression (GWLR) as a development of logistic regression model that considers spatial effect. This study used Maximum Likelihood Estimation (MLE) method to estimate the parameter model. The spatial weighting function used in this study is the Fixed Gaussian and Fixed Bisquare kernel weighting functions. The response and predictor variables in this study contain missing values, so Classification and Regression Tree (CART) method used to handle the missing values. The results showed that the best GWLR model on stunting cases modeling in Indonesia is the GWLR model with Fixed Bisquare kernel weighting function with AIC value of 622.806477 and model classification accuracy of 0.7257.