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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) The IJICS (International Journal of Informatics and Computer Science) JURNAL MAHAJANA INFORMASI Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Indonesian Journal of Education And Computer Science Jurnal Ilmiah Multidisiplin Indonesia International Journal of Industrial Innovation and Mechanical Engineering International Journal of Information Engineering and Science Bulletin of Engineering Science, Technology and Industry
Darmeli Nasution
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Social Sciences

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Journal : Jurnal Ilmiah Multidisiplin Indonesia

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Comparative Analysis of the C4.5 and Random Forest Algorithms for the Prediction of Diarrheal Disease Sipra Barutu; Muhammad Iqbal; Khairul, Khairul; Darmeli Nasution
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 4 No. 7 (2025): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), August 2025
Publisher : Sean Institute

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Abstract

Diarrhea remains one of the leading causes of death among infants in Indonesia, especially in areas with limited access to healthcare. Environmental pollution and unhealthy lifestyles are the main causes of its spread. This study aims to compare the performance of the C4.5 and Random Forest algorithms in predicting diarrhea cases among infants in the working area of the Parlilitan Subdistrict Health Center, Humbahas Regency, North Sumatra Province. Secondary data were obtained from medical records and health center reports, which were then analyzed using Python. Model performance evaluation was conducted using the metrics Accuracy, Precision, Recall, F1-Score, Specificity, False Positive Rate (FPR), and True Positive Rate (TPR). The test results showed that the C4.5 algorithm had superior performance with an Accuracy of 0.92; Precision, Recall, and F1-Score of 0.875 each; Specificity of 0.9412; and FPR of 0.0588. Meanwhile, Random Forest obtained an Accuracy of 0.88; Precision of 0.7778; Recall of 0.875; F1-Score of 0.8235; Specificity of 0.8824; and FPR of 0.1176. These findings indicate that C4.5 is more effective in maintaining a balance between prediction accuracy and detection capability, and is better at minimizing classification errors for negative classes.
Co-Authors Amrizal Lubis Asri Santosa Bela Firmantoyo Eko Hariyanto Eswin Syahputra IQBAL , MUHAMMAD Irwan Jesica Uli Panggabean Juliyandri Saragih Khairul Khairul Khairul, Khairul Khumairoh, Annisa Ludfia Anggi Safitri Sinaga Muhammad Iqbal Muhammad Iqbal Muhammad Syahputra Novelan Muhammad Wahyudi Mutiara Widasari Sitopu Rian Farta Wijaya Sinambela, Sugi Hartono Sipra Barutu Sirait, Donna Nurhaida Masdiana Siregar, Andree Rizky Yuliansyah Sitorus , Zulham Sitorus, Zulham Sugeng Pranoto Tiara Sylvia Titin Mega Andini Siahaan Tuti Andriani Yoga Yuniadi Zhafirah Rizki Fadilah Lbs Zuhri Ramadhan