Perbandingan Metode Naive Bayes dan Bayesian Regularization Neural Network Untuk Klasifikasi Jenis Penyakit Diabetes Mellitus

Filda Rahayu (Universitas Muhammadiyah Bengkulu)
Erwin Dwika Putra (Universitas Muhammadiyah Bengkulu)
Yuza Reswan (Universitas Muhammadiyah Bengkulu)
Agung Kharisma Hidayah (Universitas Muhammadiyah Bengkulu)

Jurnal Komputer, Informasi dan Teknologi
Vol. 5 No. 2 (2025): Desember

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