Comparison of Machine Learning Algorithm Performance for Toddler Stunting Prediction
DOI:
https://doi.org/10.57152/ijatis.v3i1.2503Keywords:
Early Detection, Machine Learning, Nutritional Status Classification, Random Forest, Toddler StuntingAbstract
Stunting is a chronic nutritional issue in toddlers that has long-term effects on children's physical growth and cognitive development. This study aims to compare the performance of four machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Logistic Regression (LR), in classifying the nutritional status of toddlers. The research stages included data preprocessing, data division into training and test sets, model training, and evaluation using accuracy, precision, recall, F1-Score, a confusion matrix, and Area Under the Curve (AUC). The evaluation results showed that Random Forest achieved the best performance, with an accuracy of 94%, as well as precision, recall, and F1-score values above 90%, and an AUC value close to 1.00 across all nutritional status classes. This was followed by the MLP algorithm in second place, with an accuracy of 93.29%. The main contribution of this study is the identification of a high-performing, stable model for large-scale stunting detection, providing a strong foundation for developing decision-support systems for early detection in the public health sector.
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