Modeling of the Stunting Cases Using GWPR Incorporating Exposure in Central Java Province - Triyanto Triyanto; Laila Fitriana; Getut Pramesti; Dhidhi Pambudi (ZERO: Jurnal Sains, Matematika dan Terapan, 2025)
Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression that accounts for cases of spatial heterogeneity. Many studies have been conducted on GWPR models; however, these models do not account for the population size in each region. In this study, GWPR incorporating exposure was applied for the modeling of stunting cases in Central Java Province, Indonesia. The exposure in this model is the number of toddlers in each regency/city.The results of the empirical study showed that the percentage of low birth weight has a significant effect on the stunting cases in all regencies/cities, with the exception of Purworejo and Wonosobo. Meanwhile, other independent variables that have a significant effect on stunting cases vary across regencies/cities. The GWPR model incorporating exposure yields lower MSE values than the GWPR model without exposure, which were 4871 and 5730, respectively. The lower MSE indicates that the GWPR incorporating exposure has better accuracy in modeling the number of stunting cases.