IMPLEMENTATION OF BAYES THEOREM ALGORITHM FOR WEB-BASED EXPERT SYSTEMS FOR DIAGNOSIS OF HUMAN SKIN DISEASES

Yoga Handoko  Agustin; Fitri Nuraeni; Anisa Devisa Putri

doi:10.52436/1.jutif.2024.5.1.1363

Yoga Handoko Agustin Informatics Engineering Study Program, Institut Teknologi Garut, Indonesia
Fitri Nuraeni Informatics Engineering Study Program, Institut Teknologi Garut, Indonesia
Anisa Devisa Putri Informatics Engineering Study Program, Institut Teknologi Garut, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1363

Keywords: Bayes Theorem, ESDLC, Expert, Skin, System

Abstract

The skin is an important organ in the human body which has various roles, such as functioning as a sense of touch, as a means of excretion through sweat glands, regulating body temperature, and as a place to store fat. If the care is not good, the skin can become infected, which is caused by the proliferation of bacteria, germs and viruses in the skin tissue. However, ironically, people often underestimate skin diseases because they are considered less dangerous and do not lead to death. In Garut Regency there is the Tarogong Community Health Center which is a community health center that provides health services, one of which is skin disease examination for the people of Garut. This health center has a practicing doctor, but the doctor is a general practitioner. So to provide services to people who experience skin diseases, they need help from experts. Based on these problems, this research is entitled Implementation of the Bayes Theorem Algorithm in a Web-Based Human Skin Disease Diagnosis Expert System. The Bayes theorem algorithm can determine a possibility that will occur in the future with information from the past and can later reach precise and accurate decisions and information. The methodology used in this research is the Expert System Development Life Cycle (ESDLC) which is a methodology for building or developing an expert system that is structured and directed in its work. The results of this research are in the form of an expert system application that can diagnose 14 types of skin diseases based on 26 symptoms that are often felt by the public and it was found that the results of the system diagnosis and expert diagnosis were 83.3% in agreement with 30 tests carried out with 25 appropriate data and 5 data. it is not in accordance with.

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