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

doi:10.53697/jkomitek.v5i2.2985

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

Authors

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

DOI:

https://doi.org/10.53697/jkomitek.v5i2.2985

Keywords:

Analysis, Bayesian, Diabetes, Neural, Network

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

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Published

2025-09-08

How to Cite

Filda Rahayu, Putra , E. D., Reswan , Y., & Hidayah , A. K. (2025). Perbandingan Metode Naive Bayes dan Bayesian Regularization Neural Network Untuk Klasifikasi Jenis Penyakit Diabetes Mellitus. Jurnal Komputer, Informasi Dan Teknologi, 5(2), 11. https://doi.org/10.53697/jkomitek.v5i2.2985