Implementation of the Naive Bayes Method for Stunting Classification in Children Under Five Years Old (Balita). - Ferdiansyah Sulthan Ahmad; Maulana Muhammad Farhan; Anwar Akmal Aliffandhi; Ulum Muhammad Rifki Bahrul; Diyasa I Gede Susrama; Satria Vinza Hedi (IJCONSIST JOURNALS, 2025)
The issue of stunting in Indonesia has become a serious concern, drawing significant attention from the government. To address this problem, the government has set a target to reduce the stunting prevalence rate to 14% by 2024. As an initial step in supporting this goal, the present study aims to classify the nutritional status of children under five years old using the Naive Bayes method. The objective of this research is to evaluate the performance of the Naive Bayes algorithm in classifying the nutritional status of children under five, with a focus on body weight, height, and exclusive breastfeeding intake as predictors of stunting. The research process includes several stages, namely problem formulation, data collection, data preprocessing, data splitting, model construction, model training, model evaluation, and result analysis. The findings of this study indicate that the Naive Bayes method achieved an accuracy of 70% in classifying stunting among children under the age of five, with an F1-score evaluation of 70%.