Perbandingan Metode Decision Tree dan Random Forest dalam Klasifikasi Status Gizi Balita (Studi Kasus: Puskesmas Singotrunan Banyuwangi)

Abstract

The nutritional status of toddlers is an important indicator in identifying nutritional problems in children. Based on the Banyuwangi District Health Profile, the weight-for-height index for undernutrition at the Singotrunan Community Health Center increased from 0,7% in 2022 to 2,3% in 2023. The manual process of classifying nutritional status has limitations, so a machine learning-based approach is needed. The purpose of this study is to compare the performance of the Decision Tree and Random Forest methods in classifying the nutritional status of toddlers into five classes, namely undernutrition, good nutrition, risk of overnutrition, overnutrition, and obesity. Based on the results, the Random Forest method show better performance than the Decision Tree method with an accuracy of 0,92, precision of 0,88, recall of 87, and F1-score of 0,87, while the Decision Tree method has an accuracy of 0,83, precision of 0,73, recall of 0,87, and F1-score of 0,78. Based on these results, the Random Forest method is considered more effective in classifying the nutritional status of toddlers.