Implementation of Naïve Bayes Classification Algorithm on Infant and Toddler Nutritional Status

Body weight and length are often used as a reference index in classifying the nutritional status of infants and toddlers. In Posyandu Anggrek in Limo, Depok, Indonesia, the anthropometric index calculation used is still manual, using the z-score list or WHO NCHS standard deviation found in KMS (Healthy Cards for Toddlers). Nutritional status is only seen based on the colors in the KMS without calculation or looking at the index in the anthropometric table. Naïve Bayes, one of the methods used in classifying by calculating probability data, will be used in this study to measure the nutritional status of infants and children. The variables used in evaluating the nutritional status of infants and toddlers are data about body weight and body length. Based on the results of research and discussions conducted, 46 data about infants and toddlers are divided into 90% testing data and 10% training data by type of sampling using stratified sampling at RapidMiner. Based on the data obtained, the accuracy of Naïve Bayes in classifying the nutritional status of infants and toddlers up to 75%.