Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Abstract

Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.