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All Journal Jurnal Komputer Antartika
Regen, Muhamad
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Computer Science & IT

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Deteksi Dini Stunting pada Balita Menggunakan Data Mining dengan Algoritma C4.5 Regen, Muhamad; Alamsyah, Yogi Nur; Lesmana, Rendi; Tampubolon, Asima Rodame; Hafifah, Diana Nur; Purnamawati , Annida
Jurnal Komputer Antartika Vol. 4 No. 1 (2026): Maret
Publisher : Antartika Media Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70052/jka.v4i1.1277

Abstract

Stunting is a serious issue affecting the growth and development of children in Indonesia, with a prevalence still high, reaching 148 million children under five. This study aims to develop an early detection model for stunting using the C4.5 decision tree algorithm, utilizing a large dataset containing 120,999 records that include attributes of age, height, and gender. The research method used is a quantitative experimental approach with data mining techniques, where the model was evaluated using 10-fold cross-validation to ensure accuracy and generalizability. The results show that the C4.5 model achieves 99.87% accuracy, with very high precision and recall, and good interpretability, making it suitable for implementation in public health systems. These findings emphasize the importance of height as a key indicator in detecting stunting and provide a basis for model integration in digital health initiatives in Indonesia. This study recommends incorporating socioeconomic and environmental attributes for more comprehensive analysis in the future.
Co-Authors Alamsyah, Yogi Nur Hafifah, Diana Nur Lesmana, Rendi Purnamawati , Annida Tampubolon, Asima Rodame