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All Journal Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Infotekmesin Scientific Journal of Informatics Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat TIERS Information Technology Journal Jurnal Pengabdian Mitra Masyarakat (JPMM) INOVTEK Polbeng - Seri Informatika
Ali Nur Ikhsan
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Performance Comparison of Decision Tree J48, CART, and Naïve Bayes Algorithms for Predicting Chronic Kidney Disease Ali Nur Ikhsan; Alif Nur Fadilah; Alifah Dafa Iftinani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.26472

Abstract

Chronic Kidney Disease could be a worldwide issue that proceeds to extend with high treatment costs. Accurate diagnosis is essential for managing this disease. There is a requirement for a technique to anticipate chronic kidney disease, with prevalent use being made of Decision Tree J48, Naive Bayes, and CART algorithms which offer benefits like swift computation, ease of use, and high precision. The researchers aimed to determine the comparison results of Decision Tree J48, CART, and Naive Bayes algorithms for predicting chronic kidney disease. From the research findings, it was concluded that the CART algorithm had the highest accuracy rate of 97.25% in predicting chronic kidney disease, compared to the J48 Decision Tree algorithm and the Naïve Bayes algorithm with accuracy rates of 96.5% and 93.5% respectively. The CART algorithm can be utilized by pathologists to develop a program for predicting chronic kidney disease.
Co-Authors Adam Prayogo Kuncoro Adlan Wibowo Alfiansyah Arifudin Akbar Alif Nur Fadilah Alifah Dafa Iftinani Desri Nur Astri Desty Rakhmawati Hendra Marcos Hidayah, Debby Ummul Imun Faizal Irma Darmayanti Isnaini, Khairunnisak Nur Jali Suhaman Muhammad Thoriq Jamil Muslimin Hidayat Primandani Arsi Pungkas Subarkah Raditya Sani Alifian Rayinda Maya Anjani Septi Oktaviani Nur Hidayah Siti Alvi Solikhatin Suliswaningsih Syahrul Sani Wildan Razzaq Pratama Yuli Purwati