Application of Support Vector Machine for Classification of Toddlers Nutritional Status Based on Anthropometric Data - Mohamad Alif Subhi; Rudi Kurniawan; Bani Nurhakim (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
Stunting remains a major health issue in Indonesia, especially among toddlers. This study aims to classify the nutritional status of toddlers (stunted and non-stunted) using anthropometric data from the Kaggle public dataset with the Support Vector Machine (SVM) algorithm. This dataset includes data on the height, weight, age, and gender of toddlers. It should be emphasized that the data does not originate from the Ciherang Bandung Posyandu, but rather the Posyandu is used only as a context for the potential application of the developed model. The process includes data acquisition, preprocessing (including normalization and data balancing using SMOTE), SVM model training, and evaluation with accuracy, precision, recall, F1-score, and ROC-AUC. The model was trained with an 70:30 data split and optimal parameters (C=1.0, gamma=0.01, kernel=RBF). The results showed high performance, indicating that this model can support early detection of stunting and the implementation of decision support systems in public health services.