Classification of Toddler Nutrition Status with Anthropometry Calculation using Naïve Bayes Algorithm

Growth and development process for toddler become the key point for growth and development in the next period. Nutrition needs must be given precisely, so children have a good nutrition status. Nutrition status monitoring for toddlers could be done with Anthropometry calculations, based on 3 index, weight for age (WFA), height for age (HFA), and weight for height (WFA). According to survey from Desa Tunjungtirto's Posyandu (Pos Pelayanan Terpadu/Integrated Service Post), they have not done the calculation of nutrition status, based on Anthropometry standards. To make it easier, we can use classification method. Classification is one of the data mining methods that find models or functions process that explain or differentiate the data class, its function is for predicting class from one unknown label object. Naïve Bayes is one of the popular classification algorithms and categorized as 10 best algorithms for data mining. The purpose of this research is to classified the toddlers's nutrition status based on 3 anthropometry index used Naïve Bayes algorithm. This classification will be tested with k-fold cross validation method to know the success of classification process. According to the results, can be concluded that the process of the toddler nutrition status classification, for each index, have 88% accuracy for WFA index, 64% accuracy for HFA index and 68% accuracy for WFH index.