Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19 - Nurdin Nurdin; Erni Susanti; Hafizh Al-Kautsar Aidilof; Dadang Priyanto (MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 2022)
COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.