IMPLEMENTASI METODE TEOREMA BAYES UNTUK APLIKASI DIAGNOSA PENYAKIT TENGGOROKAN TONSILITIS DAN FARINGITIS PADA MANUSIA

Abstract

Tonsillitis and pharyngitis, common throat infections, can negatively impact quality of life if not diagnosed and treated promptly, potentially leading to more serious complications. Given the urgency of early diagnosis and constraints such as limited time and accessibility to healthcare facilities that often hinder initial treatment, developing a self-diagnosis tool is crucial. Therefore, this study focuses on developing a web-based expert system that helps the general public detect tonsillitis and pharyngitis early. The system adopts Bayes' Theorem, a probabilistic approach proven effective in managing data uncertainty and generating probability estimates based on user-reported symptoms. Bayes' Theorem was chosen based on its ability to adjust the probability of a hypothesis—in this case, a disease diagnosis—when new evidence, such as additional symptoms, is introduced. This web application is designed to support early decision-making, enabling users to identify symptoms and receive early treatment recommendations. The study's findings demonstrate that the system is capable of generating diagnoses with a satisfactory level of accuracy, demonstrating its potential as a reliable tool for providing initial guidance and encouraging the public to consult medical professionals when necessary.