Untitled

Abstract

The problem of nutritional status in toddlers is still a major problem that needs attention, one of which is malnutrition. Malnutrition problems are directly caused by inadequate food intake, inadequate family economic factors, and caused by underlying diseases such as tuberculosis, heart disease or birth defects. The purpose of this study is to implement the clustering method with the K-Means and Fuzzy C-Means algorithms which aims to evaluate the nutritional status of children under five in general so that it can be used as a basis for early prevention for health workers at the puskesmas to overcome malnutrition. This study uses 4 parameters, namely height, weight, genderand age and uses a silhouette index validation calculation to measure the resulting cluster cohesiveness. From the result of the cluster analysis, the K-Means algorithm gets a validation result of 0.79 with 4 clusters and the Fuzzy C-Means algorithm gets a validation result of 0.78 with 4 clusters. From the two comparison algorithm, the K-Means algorithm gets the best validation because get the highest validation compared to the Fuzzy C-Means algorithm.