📅 28 February 2022
DOI: 10.35671/telematika.v15i1.1307
An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes
Telematika
Universitas Amikom Purwokerto
📄 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.
🔖 Keywords
#optimize weights; naïve bayes; diabetes; genetic algorithm
ℹ️ Informasi Publikasi
Tanggal Publikasi
28 February 2022
Volume / Nomor / Tahun
Volume 15, Nomor 1, Tahun 2022
📝 HOW TO CITE
Somantri, Oman; Politeknik Negeri Cilacap; Maharrani, Ratih Hafsarah; Politeknik Negeri Cilacap; Wanti, Linda Perdana; Politeknik Negeri Cilacap; , "An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes," Telematika, vol. 15, no. 1, Feb. 2022.
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