Nutrition Classification In Toddlers at UPTD Puskesmas Tigaraksa Using A Comparison of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Methods

Toddler are a group of people who vulnerable to nutritional problems. If the incidence malnutrition is not addressed, it will have negative impact on children under five, condition experienced by person due lackof intake amount nutrients consumedis below. Health centers required improve and organize helath services as well possible therefore researchers conduct research at UPTD Tigaraksa Center doing comparison classification results toddler data using Support Vector Machine K-Nearest Neighbor methods WEKA Tools. Based result between Negihbor Tools carrying out 5 (five) stages testing namely : Use Training Set, 4 Cross-Validation, 8 50% Percentage Split dan 80% Split, show that method Kernel Radial Basis Function (RBF) an average accuracy value 100% higher than Euclidean Distnace algorithm with 93%.