Modelling the Prevalence of Stunting in Toddlers Aged 6 – 23 Months in Indonesia with Approaches Multivariate Adaptive Regression Splines and Generalized Additive Model - Aflaha, Nabila Shafa; Oktavia, Sabrina Salsa; Kurniawan, Ardi (Universitas Muhammadiyah Mataram, 2026)
Stunting remains a major global public health issue, marked by growth failure caused by long-term nutritional deficiencies in early childhood. In Indonesia, stunting prevalence among children under five was reported at 21.5% in 2023. This study employs an analytical observational approach with a cross-sectional design to examine nutritional factors associated with stunting among children aged 6–23 months in Indonesia, using Multivariate Adaptive Regression Splines (MARS) and Generalized Additive Models (GAM). Secondary data were obtained from the 2024 Indonesian Nutritional Status Survey (SSGI), encompassing 36 provinces. Stunting prevalence was defined as the response variable, while predictor variables included the consumption of animal-source protein, sweetened beverages, unhealthy foods, and the lack of fruit and vegetable intake. The analysis began with descriptive statistics and was followed by MARS and GAM modelling. Model performance was assessed using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The findings indicate that the GAM model outperformed MARS, achieving a higher R² 0.7734 and a lower RMSE 2.5968, compared to MARS with an R² of 0.7319 and an RMSE of 2.8249. While MARS effectively identified structural changes through hinge functions, GAM offered greater modelling flexibility via smooth functions. Among the examined factors, animal-source protein intake showed the strongest association with stunting, followed by the consumption of sweetened beverages and unhealthy foods, whereas inadequate fruit and vegetable intake exhibited a weaker relationship. Overall, both approaches were effective, although GAM demonstrated superior predictive capability for provincial-level stunting analysis.