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

Abstract

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