Open AccessTHE DESIGN OF EXPERT SYSTEM FOR DETERMINING THE INITIAL DIAGNOSIS OF TROPICAL INFECTIOUS DISEASES IN INDONESIA WITH NAIVE BAYES METHOD-BASED ANDROID
Author(s) -
Andrew Dwi Permana,
I Made Arsa Suyadnya,
Duman Care Khrisne
Publication year - 2019
Language(s) - English
Resource type - Journals
ISSN - 2579-5538
DOI - 10.21460/jutei.2018.22.112
Subject(s) - dengue fever , naive bayes classifier , confusion matrix , measles , malaria , typhoid fever , usability , confusion , computer science , tropical disease , tuberculosis , bayes' theorem , medicine , artificial intelligence , bayesian probability , immunology , disease , psychology , virology , pathology , human–computer interaction , support vector machine , psychoanalysis , vaccination
Tropical infectious diseases are frequent, serious and concerning for the people in Indonesia. Tropical infectious diseases can be fatal and cause death. But if we diagnose them earlier and get proper treatment, the story can be changed. In this research will make a mobile application using Naive Bayes and Forward Chaining methods for early diagnosing tropical infectious diseases including typhoid fever, dengue fever, tuberculosis, malaria, and measles. The process of this application will start with input of the symptoms felt by users, after the data collected, system will calculate the data with Naive Bayes formula. This application using 147 data training from interviewed with the experts. Based on the tests by System Usability Scale method shows above average users rating 73.875 %, which means the results of the application are acceptable. And Confusion Matrix method shows performance of the application as high as 76.74 %.