CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING SUPPORT VECTOR MACHINE AND RANDOM FOREST TECHNIQUES WITH OPTIMAL FEATURE SELECTION - Widyawati, Femmi; Suhito, Hanif Pandu; Yassin, Warusia; Agus Santoso, Heru (Informatika, Universitas Jenderal Soedirman, 2024)
Nutritional problems in toddlers, such as stunting, wasting, being underweight, and obesity, are major challenges in monitoring toddler health in Indonesia because they can hurt toddler growth and development. Therefore, handling nutritional problems comprehensively, including prevention efforts and appropriate dietary interventions, is very important. This study aims to develop a toddler nutritional status classification model based on machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest, by utilizing a toddler dataset obtained from Health Institutions in Indonesia containing 9,735 data. The model was designed using the Recursive Feature Elimination (RFE) technique for selecting relevant features and the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. The results showed that the Random Forest algorithm performed best with 95% accuracy, 77% precision, 87% recall, and 81% f1-score. This study contributes to developing a machine learning-based approach to support a more effective nutritional monitoring system and enable more appropriate dietary interventions to address toddler health problems in Indonesia.