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Glorynta S B Nadeak Glorynta S B Nadeak
Universitas Pembangunan Panca Budi
Computer Science & IT

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Analysis of Dempster Shafer Method, Certainty Factor and Bayes Theorem in Expert Systems Diagnosing Tuberculosis Disease Khairul Khairul; Rian Farta Wijaya; Rizky Rinaldi Rizky; Rahmat Rezki Rahmat Rezki; Siska Simamora; Glorynta S B Nadeak Glorynta S B Nadeak; Reza Fahromi Reza Fahromi
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

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Abstract

This research discusses the design of an expert system that specifically deals with the problem of pulmonary tuberculosis. Tuberculosis or abbreviated TB / TB is an infectious disease infectious disease caused by bacteria from the Mycobacterium group, viz Mycobacterium tuberculosis. According to Infodatin Center for Data and Information Ministry of Health of the Republic of Indonesia (2016) tuberculosis or TB can attacks various organs, especially the lungs, which if not treatment or treatment is not complete then it can cause complications dangerous to death. Seeing the phenomena that occur is very much needed precise and easy information on pulmonary tuberculosis by developing an Artificial Intelligence technology, namely the Expert System. In the application of Expert Systems used to diagnose Pulmonary tuberculosis needs to be compared several methods including: Certantiy Factor, Dempster Shafer, and Bayes theorem so that later it can be known which method most appropriate and best in making a diagnosis. With this Expert System, it can later be used as a consulting service to assist in diagnosing the type of pulmonary tuberculosis based on clinical symptoms that occur in patients, so that it can be used in making initial diagnostic conclusions before carrying out intensive laboratory examinations.
Co-Authors Khairul Rahmat Rezki Rahmat Rezki Reza Fahromi Reza Fahromi Rian Farta Wijaya Rizky Rinaldi Rizky Simamora, Siska