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Jurnal Sisfokom (Sistem Informasi dan Komputer)Jurnal Sisfokom (Sistem Informasi dan Komputer)

Stunting remains a serious public health issue in Indonesia, with persistently high prevalence and long-term impacts on childrens 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. 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. 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.

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.

Berdasarkan penelitian ini, beberapa saran penelitian lanjutan dapat diajukan untuk memperkuat pemahaman dan penerapan machine learning dalam pencegahan stunting di Indonesia. Pertama, perlu dilakukan penelitian lebih lanjut mengenai integrasi data spasial-temporal yang lebih komprehensif, termasuk faktor lingkungan dan perubahan iklim, untuk meningkatkan akurasi prediksi dan mengidentifikasi wilayah-wilayah yang paling rentan terhadap stunting. Kedua, pengembangan model yang lebih transparan dan mudah diinterpretasikan (Explainable AI/XAI) sangat penting agar hasil prediksi dapat dipahami dan dimanfaatkan secara efektif oleh para pembuat kebijakan dan tenaga kesehatan di lapangan. Ketiga, validasi model secara lintas wilayah sangat diperlukan untuk memastikan generalisasi dan keandalan model dalam berbagai konteks geografis dan sosial-ekonomi, sehingga intervensi pencegahan stunting dapat dirancang secara lebih tepat sasaran dan berkelanjutan.

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