Article Per Year (5 Year)
p-Index From 2021 - 2026
1.819
P-Index
This Author published in this journals
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

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Scientific Journal of Engineering Research

 1 
Comparison of Machine Learning Algorithms for Stunting Classification Yunus, Muhajir; Biddinika, Muhammad Kunta; Fadlil, Abdul
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.9

Abstract

Indonesia is one of the countries with medium stunting data over the past decade, around 21.6%. Stunting prevention is a national program in Indonesia, and stunting reduction in children is the first of the six goals in the Global Nutrition Target for 2025. Based on SSGI data in 2022, the prevalence of stunting in Gorontalo Province is 23.8% and is in the high category. Stunting prevention is an early effort to improve the ability and quality of human resources. This study compared two Machine Learning algorithms for stunting classification in children, namely the Naive Bayes method and Decision Tree C4.5 using Python by dividing the training and testing data a total ratio of 80:20. The performance of each algorithm was evaluated using a dataset of child health information based on z-score calculation data with a total of 224 records, consisting of 4 attributes and 1 label, namely gender, age, weight, height and nutritional status. The results of the research that have been conducted show that the Decision Tree C4.5 algorithm achieves the highest accuracy in the classification of stunting events with an accuracy of 87% while for the Naïve Bayes algorithm produces a low accuracy of 71% so that for this study the Decision tree C4.5 algorithm is the best algorithm for the classification of stunting events. These findings suggest this algorithm can be a valuable tool for classifying children's stunting.
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