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Filda Rahayu
Universitas Muhammadiyah Bengkulu
Computer Science & IT Education Languange, Linguistic, Communication & Media

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Perbandingan Metode Naive Bayes dan Bayesian Regularization Neural Network Untuk Klasifikasi Jenis Penyakit Diabetes Mellitus Filda Rahayu; Erwin Dwika Putra; Yuza Reswan; Agung Kharisma Hidayah
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 2 (2025): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i2.2985

Abstract

Diabetes Mellitus is associated with long-term damage, dysfunction, and failure of various organs, especially the eyes, kidneys, nerves, heart, and blood vessels. Naive Bayes is a classification method that can predict the probability of a class, thus generating decisions based on learning data. The Naive Bayes method is used to classify Diabetes Mellitus. To predict a disease using a data mining approach, symptoms accompanied by clinical data are required. Therefore, the problem is formulated how the Naive Bayes method compares with Bayesian regularization neural networks for classifying types of Diabetes Mellitus. With the RapidMiner tool, it becomes educational information in providing information on Diabetes Mellitus based on Type 1 Diabetes, Type 2 Diabetes, and Gestational Diabetes
Co-Authors hidayah, agung kharisma Putra, Erwin Dwika Yuza Reswan