A Gaussian Mixture Model Approach to Profiling Stunting Risk Across Indonesian Provinces

Stunting is still a major health problem in Indonesia, with notable differences between provinces. Although the national rate has decreased over time, regional gaps continue, emphasizing the role of data in helping to explain what contributes to the issue. This study aims to segment 38 provinces in Indonesia based on maternal and child health indicators associated with stunting prevalence. The variables used include the percentage of low birth weight (LBW) infants, the percentage of infants born short, the percentage of pregnant women with chronic energy deficiency (CED), exclusive breastfeeding (EBF) coverage, prevalence of diarrhea in toddlers, and prevalence of acute respiratory infections (ARI) in toddlers. The clustering analysis was performed using the Gaussian Mixture Model (GMM) with the number of clusters varied from 2 to 7. Model selection was based on the Bayesian Information Criterion (BIC), where the lowest value indicated the optimal model. The results show that the model with two clusters was selected, with a BIC value of 1358.24, which indicates the best balance between model fit and complexity. This clustering reveals that provinces are grouped based on similarities in maternal and child health profiles, not on geographic proximity, meaning that the GMM method does not rely on spatial location to form clusters.