ANALISIS PREDIKSI TUMBUH KEMBANG ANAK DENGAN MACHINE LEARNING - Nugraheni, Murien; Widodo, Widodo; Lestari, Uning; Effendy, Vina Ardelia; Yunanto, Prasetyo Wibowo; Amannu, Ramadhan (Infotech: Journal of Technology Information, 2025)
Stunting is a major chronic nutritional issue that remains a significant challenge in Indonesia. This study aims to predict the risk of stunting in children and enhance prevention efforts by analyzing the health and nutritional status of parents. The research employs Machine Learning methods by comparing the performance of the Decision Tree and Gaussian Naive Bayes algorithms. The dataset was obtained from open data sources and analyzed using Google Colab, with a Technology Readiness Level (TRL) of level 3. Evaluation results show that both algorithms achieved an accuracy of 95.35% based on the confusion matrix. The model accurately identified 2 stunting cases (True Positive) and 41 non-stunting cases (True Negative), indicating a high level of classification reliability. These findings suggest that Machine Learning approaches can be effectively utilized as early detection tools to support stunting prevention strategies in children.