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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INFOKUM Cendikia : Media Jurnal Ilmiah Pendidikan Jurnal Info Sains : Informatika dan Sains Jurnal Komputer Indonesia (JU-KOMI) Jurnal Manajemen, Akuntansi dan Rumpun Ilmu Ekonomi (MAR-Ekonomi) Pengabdian Kepada Masyarakat Indonesia SEAN (ABDIMAS SEAN) "Journal of Data Science
Simamora, Siska
SEAN Instiute, Sumatera Utara, Indonesia
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Other

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Journal : Jurnal Komputer Indonesia (JU-KOMI)

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Implementation of C4.5 Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.751

Abstract

Diarrheal disease remains one of the major health problems among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, mothers’ hand hygiene, and immunization status play an important role in influencing the occurrence of diarrhea. This study aims to analyze the application of the C4.5 algorithm in developing a predictive model for diarrhea among toddlers using secondary data from a Public Health Center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The analysis process was carried out through entropy calculation, information gain assessment, and decision tree construction to obtain classification patterns. The results showed that the C4.5 model achieved an accuracy of 92%, precision of 87.5%, recall of 87.5%, F1-score of 87.5%, and specificity of 94.12%. These values indicate that the C4.5 algorithm is capable of making predictions with a good level of accuracy and balance in detecting both positive and negative cases. This study contributes to the utilization of data mining, particularly the C4.5 algorithm, as a decision-support tool in the health sector for the prevention of diarrheal disease among toddlers.
Implementation of Random Forest Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.753

Abstract

Diarrhea is one of the leading causes of morbidity among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, maternal hand hygiene, and immunization status contribute significantly to the incidence of diarrhea. This study aims to analyze the application of the Random Forest algorithm in developing a predictive model for diarrhea in toddlers using secondary data from a community health center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The model was constructed by generating multiple decision trees and combining them using a majority voting technique. The results show that the Random Forest algorithm achieved an accuracy of 88%, precision of 77.78%, recall of 87.5%, F1-score of 82.35%, and specificity of 88.24%. These values indicate that Random Forest is quite reliable in detecting positive diarrhea cases, although some limitations remain in reducing misclassification of negative data. This study contributes to the utilization of machine learning algorithms, particularly Random Forest, as a decision-support tool in the health sector for diarrhea prevention among toddlers.
Co-Authors Amran Manalu Andysah Putera Utama Siahaan Glorynta S B Nadeak Glorynta S B Nadeak Hasugian , Paska Marto Khairul Khairul Khairul, Khairul Mathelinea, Devy Muhammad Iqbal Rahmat Rezki Rahmat Rezki Reza Fahromi Reza Fahromi Rian Farta Wijaya Rizky Rinaldi Rizky Sihotang, Jonhariono Simangunsong, Pandi Barita Nauli Sipra Barutu Sitorus, Zulham Zahari Amir Bin Ismani