Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting

Febi Vianti; Hani Khaulasari; Yuniar Farida; Cicik Swantika; Havid Efendi

doi:10.37905/euler.v12i2.28072

Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting

Febi Vianti, Hani Khaulasari, Yuniar Farida, Cicik Swantika, Havid Efendi

Abstract

Stunting is a child growth disorder that is evident from a lack of height for age. Jember Regency has a stunting prevalence rate of 34.90% in 2022, making it the region with the highest stunting cases in East Java. The purpose of this research is to map the factors that influence stunting in Jember Regency with a spatial analysis approach. The method applied in this study is Geographically Weighted Regression (GWR) to analyze the spatial relationship between predictors and responses. GWR uses an optimal kernel to determine the spatial weights based on distance accurately, as well as the AIC and goodness criteria to calculate the goodness of the model. The research variables include the number of stunting cases in Jember Regency as the response variable (Y), and the predictor variables (X) are chronic energy deficiency pregnant women (), anemic pregnant women (), exclusive breastfeeding (), proper sanitation (), pregnant women consuming TTD at least 90 days (), complete basic immunization (), and wasting (). The results of the study using the adaptive gaussian kernel with the minimum CV compared to other kernels can improve accuracy, so it can be applied to data analysis. The GWR model obtained an accuracy of 80.59% and AIC 360. indicates the ability to explain 80.59% of the variability of the response data, and the AIC value is 360, which reflects the efficiency and suitability of the model to spatial data. From the GWR parameters, 14 groups were formed where there are several different factors in each area in the sub-districts in Jember Regency.

Keywords

Geographically Weighted Regression; Adptive Gaussian; Stunting

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DOI: https://doi.org/10.37905/euler.v12i2.28072