Application of Naïve Bayes Classifiers for Family Risk Identification and Stunting Intervention Planning - Kurniawan, Wildan Indra; Triloka, Joko (Journal of Applied Informatics and Computing, 2025)
Stunting remains a significant public health concern influenced by a combination of social, economic, and environmental factors. This study aims to implement the Naïve Bayes algorithm to support the determination of appropriate intervention strategies for families identified as being at risk of stunting in Metro City. Risk data were obtained from the BKKBN Metro City and underwent preprocessing steps, including handling missing values, encoding categorical variables, and feature selection. The dataset was then divided into training, validation, and testing subsets to develop and evaluate models using three Naïve Bayes variants: Gaussian, Multinomial, and Bernoulli. Evaluation metrics of accuracy, precision, recall, and F1-score indicate that the Multinomial Naïve Bayes model achieved the best performance with 99% accuracy, followed by the Bernoulli Naïve Bayes model with 98% accuracy. Both models effectively classified families at risk of stunting with minimal misclassification, while the Gaussian Naïve Bayes variant demonstrated lower performance with an accuracy of 60%. These results highlight the potential of the Naïve Bayes algorithm, particularly the Multinomial and Bernoulli models, as practical and efficient tools to support data-driven decision-making for stunting interventions.