DOI: https://doi.org/10.14710/j.gauss.14.1.42-53

COMPARISON OF NEGATIVE BINOMIAL SAR, SEM, AND SARMA METHODS IN MODELING THE NUMBER OF MALNUTRITION CASES AMONG TODDLERS IN CENTRAL JAVA

*Andi Rosilala orcid  -  Department of Statistics, Faculty of Science and Technology, Universitas Terbuka, Tangerang Selatan, Banten, Indonesia
Siti Hadijah Hasanah orcid  -  Department of Statistics, Faculty of Science and Technology, Universitas Terbuka, Tangerang Selatan, Banten, Indonesia
Open Access Copyright 2025 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Malnutrition among toddlers remains a critical public health issue in Indonesian, with high rates of stunting, wasting, and underweight. The 2023 Indonesian Health Survey in Central Java reports 20.7% stunting, 7.1% wasting, 14.4% underweight, and 4.2% overweight, closely reflecting national trends. This study employs spatial analysis to examine malnutrition patterns at Central Java, finding overdispersion in the data (mean = 69.8; variance = 1033.4), which supports the use of the Negative Binomial model over Poisson. Moran’s I and Getis-Ord G tests confirm spatial dependence. Among the spatial models tested—SAR, SEM, and SARMA—the SARMA-NB model was the most suitable, with optimal fit measures (AIC = 299.37, MAD = 9.74, MAPE = 15.88, RMSE = 12.77). Significant predictors include the percentage of children with complete immunization, exclusive breastfeeding rates, per capita meat consumption, access to sanitation, health insurance with contribution assistant coverage, poverty levels, low birth weight incidence, and inadequate housing. The findings emphasize the importance of spatial analysis in understanding malnutrition risk factors.

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Keywords: Negative Binomial; Overdispersion; Spatial Regression

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