Research Article
Stunting Prediction in South Sumatra Province, Indonesia: Machine Learning Approaches Using SKI 2023
@INPROCEEDINGS{10.4108/eai.6-11-2025.2364517, author={Alfensi Faruk and Dian Cahyawati and Endang Sri Kresnawati}, title={Stunting Prediction in South Sumatra Province, Indonesia: Machine Learning Approaches Using SKI 2023}, proceedings={Proceedings of the 4th Sriwijaya International Conference on Basic and Applied Sciences, SICBAS 2025, 6 November 2025, Palembang, Indonesia}, publisher={EAI}, proceedings_a={SICBAS}, year={2026}, month={8}, keywords={Stunting Prediction under-five children national survey data logistic regression gradient boosting machine}, doi={10.4108/eai.6-11-2025.2364517} }- Alfensi Faruk
Dian Cahyawati
Endang Sri Kresnawati
Year: 2026
Stunting Prediction in South Sumatra Province, Indonesia: Machine Learning Approaches Using SKI 2023
SICBAS
EAI
DOI: 10.4108/eai.6-11-2025.2364517
Abstract
Stunting is still one of the main health issues in Indonesia, including in South Sumatra Province. This study utilises logistic regression and gradient boosting machines using the Indonesian Health Survey (SKI) 2023 under-five children microdata (n = 2,940). The stunting prevalence in South Sumatra had decreased from 20.3% in 2023 to 15.9% in 2024. However, more effort is still required to achieve the national target. Our results show that birth weight, birth length, living conditions, sanitation, and the child’s sex are factors that significantly affect stunting, confirmed also by SHapley Additive exPlanations (SHAP) analysis. Both models show modest discrimination capability (AUROC ≈ 0.59). Our findings are informative to enhance the strategies of stunting prediction.
