A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024)

Emilda Indrisari; Hidayat Febiansyah; Bambang Adiwinoto

doi:10.32736/sisfokom.v14i3.2366

Authors

Emilda Indrisari Faculty of Information Technology, ISB Atma Luhur
Hidayat Febiansyah Faculty of Information Technology, ISB Atma Luhur
Bambang Adiwinoto Faculty of Information Technology, ISB Atma Luhur

DOI:

https://doi.org/10.32736/sisfokom.v14i3.2366

Keywords:

Stunting, Machine Learning, Nutrition Prediction, Spatial Data, Health Classification

Abstract

Stunting remains a serious public health issue in Indonesia, with persistently high prevalence and long-term impacts on children's physical and cognitive development. The growing need for data-driven early detection systems has encouraged the adoption of technologies such as machine learning (ML) to more effectively predict stunting prevalence. This study employed a Systematic Literature Review (SLR) to examine 20 scientific articles published between 2020 and 2024, focusing on the application of ML algorithms in stunting research. Literature was sourced from Scopus and Google Scholar, with inclusion criteria covering studies relevant to the Indonesian context or comparable global settings. The analysis focused on the algorithms used, data types, model performance, and implementation challenges. The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. The main challenges include data imbalance, limited model interpretability, and a lack of external validation. In conclusion, machine learning holds strong potential to support predictive systems and data-driven policies for stunting prevention in Indonesia. This study recommends future research to focus on integrating spatial-temporal data, implementing Explainable AI (XAI), and conducting cross-regional validation to enhance model reliability and policy relevance.

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