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All Journal JOIN (Jurnal Online Informatika) Journal of Technopreneurship and Information System (JTIS) Jurnal Pengabdian Masyarakat Bumi Raflesia Journal of Robotics and Control (JRC) Jurnal Teknik Informatika (JUTIF) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal INFOTEL IJMST Scientific Journal of Engineering Research
Muhajir Yunus
Universitas Muhammadiyah Gorontalo
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Classification of Stunting in Children Using the C4.5 Algorithm Yunus, Muhajir; Biddinika, Muhammad Kunta; Fadlil, Abdul
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1062

Abstract

Stunting is a disease caused by malnutrition in children, which results in slow growth. Generally, stunting is characterized by a lack of weight and height in young children. This study aims to classify stunting in children aged 0-60 months using the Decision Tree C4.5 method based on z-score calculations with a sample size of 224 records, consisting of 4 attributes and 1 label, namely Gender, Age, Weight, Height, and Nutritional Status. The results of the study obtained a C4.5 decision tree where the Age variable influenced the classification of stunting with the highest Gain Ratio of 0.185016337. Meanwhile, the evaluation of the model using the Confusion matrix resulted in the highest accuracy of 61.82% and AUC of 0.584.
Predicting Underweight Toddlers in Gorontalo Province Using Supervised Learning Algorithms Yunus, Muhajir; Nurdin, St Suryah Indah; Fitriah, Fitriah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5331

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.
Co-Authors Abdul Fadlil Abdul Fadlil Abdul Fadlil Ahmad Fatoni Dwi Putra Alwas Muis Asno Azzawagama Firdaus Azminuddin I. S. Azis Daniel Santoso Faresta, Rangga Alif Fitriah Fitriah Fitriah Fitriah G. Tangaro, Diana May Glaiza Grancho, Bernadine Hadi San, Ahmad Syahrul Hasanah, Rakyatol Herman Herman Imam Riadi Imam Riadi Muhammad Kunta Biddinika Muhammad Kunta Biddinika Muhammad Kunta Biddinika Muis, Alwas Munzir, Misbahul Muzakkir Pangri Nurdin, St Suryah Indah Rusydi Umar Sani SR, Sahrul Sulton, Chaerus Sumarno Wijaya Suprihatin Suprihatin Talirongan, Florence Jean B. Yuan Sa'adati