Perbandingan Penggunaan Certainty Factor dan Pendekatan Dempster-Shafer dalam Sistem Expert untuk Mendiagnosis Kasus Cacar

Abstract

Varicella, another name for smallpox, is a viral infection that primarily affects children and can cause an itchy rash on the skin and fever. Smallpox should be diagnosed as soon as possible to stop the spread of the disease and provide appropriate treatment. The aim of this study was to compare the Dempster-Shafer and Certainty Factor (CF) approaches for diagnosing smallpox. The main aim of this study was to evaluate the effectiveness of both methods in detecting smallpox and to determine which is more accurate and reliable in the diagnosis process. The CF method is an approach in artificial intelligence that uses confidence factor values to describe an expert's level of confidence in an event or statement. Meanwhile, Dempster Shafer is a combined theory that can overcome uncertainty in decision making by modeling the level of confidence in various aspects. This research will outline the basic concepts of the Certainty Factor method and Dempster-Shafer Theory, as well as analyze their application when making a diagnosis of smallpox. The level of precision, dependability, and effectiveness of each technique will be compared. It is hoped that health professionals can improve smallpox diagnosis and make better clinical judgments with the help of the results of this study. The results of this research will help medical personnel and health practitioners make better decisions in diagnosing smallpox. Apart from that, this research can also help reduce diagnostic errors and speed up the treatment process. The calculation results from this research show that for Shingles, the Dempster Shafer approach produces a success rate of 86%, while the Certainty Factor method offers a success rate of 99%.