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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) SemanTIK : Teknik Informasi Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Techne : Jurnal Ilmiah Elektroteknika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Terapan (J-TIT) International Journal of Informatics and Computation Jurnal Informatika dan Rekayasa Perangkat Lunak Respati Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Computer Science and Information Technology (CoSciTech) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Mohammad Diqi
Universitas Respati Yogyakarta
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Comparative Analysis of Kidney Disease Detection Using Machine Learning DIQI, MOHAMMAD; ORDIYASA, I WAYAN; HISWATI, MARSELINA ENDAH
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 2 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i2.21468

Abstract

This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.
Co-Authors Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Arum Kurnia Sulistyowati Azijah, Izattul Bagus Subekti Nuswantoro Basuki, Umar Dian Rhesa Rahmayanti Dyan Avando Meliala Eka Damayanti Elisabeth Deta Lustiyati Ema Utami Erizal Erizal Farida Nur Aini Fathinah, Azzah FAUZIYYAH, ANNUR Ferry Wahyu Wibowo HAFIZAH, IDA Hamzah Hamzah Hamzah Husna Arwa Salsabil I Wayan Ordiyasa Ikhwan Mustiadi Indra Listiawan Jelina Imlabla Kristian, Tadem Vergi Kusrini, Kusrini Lestari, Sri Listiawan, Indra Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mujastia Muhdalifah Noverianus, Noverianus nur aini, farida Ordiyasa, I Wayan Putra Wanda Putra Wanda, Putra Romana Herlinda Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sri Lestari Sriwidodo Sriwidodo, Sriwidodo Subandi, Yeyen Sugeng Winardi Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Widyaningrum, Rini Wijaya, Nurhadi Wiratma, Harits Dwi Zaidir