Article Per Year (5 Year)
p-Index From 2019 - 2024
2.225
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTEK: Informatika dan Teknologi Informasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknologi Informasi dan Pendidikan Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Minfo Polgan (JMP) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat
Ratih Hafsarah Maharrani
Politeknik Negeri Cilacap
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Telematika

 1 
An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes Oman Somantri; Ratih Hafsarah Maharrani; Linda Perdana Wanti
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1307

Abstract

This research proposes a method to optimize the accuracy of the Naïve Bayes (NB) model by optimizing weight using a genetic algorithm (GA). The process of giving optimal weight is carried out when the data will be input into the analysis process using NB. The research stages were conducted by preprocessing the data, searching for the classic naïve Bayes model, optimizing the weight, applying the hybrid model, and as the final stage, evaluating the model. The results showed an increase in the accuracy of the proposed model, where the naïve Bayes classical model produced accuracy rate of 87.69% and increased to 88.65% after optimization using GA. The results of the study conclude that the proposed optimization model can increase the accuracy of the classification of early detection of diabetes.
Co-Authors Abdul Rohman Supriyono Agus Susanto Annas Setiawan Prabowo Annisa Romadloni Eka Tripustikasari Fadillah Fadillah Ganjar Ndaru Ikhtiagung Hani Hidayati Hani Hidayati Hety Dwi Astuti Hety Dwi Hastuti Ike Yunia Pasa Laura Sari Linda Perdana Wanti Lutfi Syafirullah Muhammad Nur Faiz Nur Wachid Adi Prasetya Oman Somantri Prih Diantono Abda'u Prih Diantono Abda`u Riyadi Purwanto Rostika Listyaningrum Santi Purwaningrum