Memanfaatkan Pendekatan SVM untuk Klasifikasi Status Gizi Balita Berdasarkan Pengukuran Antropometri - Arif Wicaksono Septyanto; Henokh Lugo Hariyanto; Hanifah Permatasari (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2023)
The system to assess the nutritional status of toddlers is crucial for monitoring their nutritional growth. This research employs the Support Vector Machine (SVM) approach with the aim of classifying the nutritional status of toddlers based on anthropometric indices. The data used for classification includes gender, age, weight, height, and body mass index. The nutritional status data for toddlers is calculated based on anthropometric indices, including Weight for Age (WFA), Height for Age (HFA), Weight for Height (WFH), and Body Mass Index for Age (BMIFA). Accuracy calculation utilizes 723 toddler data as training data and 311 toddler data as test data. The classification results demonstrate that the Support Vector Machine (SVM) method achieves an accuracy of 91.91%, with a Recall of 99.86% and Precision of 91.94%.