Predicting Underweight Toddlers in Gorontalo Province Using Supervised Learning Algorithms

Muhajir Yunus; St Suryah Indah Nurdin; Fitriah Fitriah

doi:10.52436/1.jutif.2025.6.5.5331

Authors

Muhajir Yunus Digital Business, Universitas Muhammadiyah Gorontalo, Indonesia
St Suryah Indah Nurdin Midwifery, Universitas Muhammadiyah Gorontalo, Indonesia
Fitriah Informatics, Universitas Muhammadiyah Bengkulu, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5331

Keywords:

Decision Tree C4.5, K-Nearest Neighbor, Naïve Bayes, Supervised Learning, Toddlers, Underweight

Abstract

Malnutrition in toddlers, notably underweight, remains a critical public health issue in Indonesia. According to the 2023 Indonesian Health Survey, the prevalence of underweight among toddlers has reached 15.9%. This condition has a significant impact on children's physical growth, cognitive development, and overall quality of life. This study aims to develop a predictive model for early detection of toddler nutritional status using three supervised machine learning algorithms: Decision Tree C4.5, K-Nearest Neighbor, and Naïve Bayes. The dataset consisted of 9,284 toddler records from Gorontalo Province, comprising eight attributes and one class label indicating nutritional status. Evaluation results showed that the Decision Tree C4.5 algorithm delivered the best performance with 98.56% accuracy. The K-Nearest Neighbor model achieved an accuracy of 97.99%, while the Naïve Bayes model obtained 96.96%. These findings demonstrate that machine learning can be an effective tool for identifying toddlers at risk of undernutrition early in their development. Beyond individual predictions, the proposed model represents a significant advancement in health informatics by providing a scalable decision-support system. This system can enhance the efficiency and precision of public health interventions, enabling faster, data-driven responses to combat malnutrition and improve child health outcomes across broader populations.

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