Classification of Toddler Stunting Status Using Naïve Bayes Classifier with K-Fold Cross Validation

Abstract

The growth of toddlers that doesn’t meet age standards can affect their quality of life in the future. In Indonesia, one of the nutritional problems that continues to receive significant attention is stunting, which is generally indicated by a mismatch between a child's height and age (height-for-age, HAZ/TB/U). Considering that determining stunting status requires a high level of accuracy, a data-driven approach is needed to support the identification and evaluation of children's nutritional conditions in a more systematic manner. This study aims to classify stunting status among toddlers using the Naïve Bayes Classifier (NBC) algorithm with a 10-Fold Cross Validation method. NBC was selected because it is simple, efficient, and suitable for probability-based classification of health data. The data were obtained from the Health Office of Pasaman Regency in 2022. The variables used include gender, birth weight, birth height, age at measurement, body weight, body height, Mid-Upper Arm Circumference (MUAC), and height-for-age (HAZ) status. Stunting status was divided into two categories: stunted and severely stunted. The analysis showed that 53.91% of toddlers were classified as stunted, while 46.10% were classified as severely stunted. Based on the evaluation using 10-Fold Cross Validation, the NBC model showed good performance with an accuracy of 86,26%, precision of 73,33%, recall of 68,75%, and an F1-score of 70,97%. The analysis also showed that height, weight, and MUAC at the time of measurement were the characteristics that most clearly distinguished the stunted and severely stunted categories. These findings indicate that anthropometric indicators can support the detection and monitoring of stunting among toddlers. Overall, this study is expected to help health workers identify toddlers who require further nutritional assessment and support more targeted stunting management efforts.