Sistem Pakar Diagnosa Penyakit Gigi Menggunakan Metode Forward Chaining Dengan Probabilitas Teorema Bayes

Abstract

Dental diseases often go undetected in their early stages due to low public awareness and limited access to healthcare services. This study aims to develop a web-based expert system capable of performing early self-diagnosis of dental diseases. The system utilizes the Forward Chaining method to match symptoms with predefined rules in the knowledge base, and applies Bayes' Theorem to calculate the probability and confidence level of each possible diagnosis. The system was developed using the PHP Laravel framework and MySQL database. System evaluation was conducted through black-box testing to verify functionality, and accuracy testing by comparing system results with expert diagnoses. The testing results showed an accuracy rate of 90%, with a precision of 91%, recall of 89%, and F1-score of 90%. These findings demonstrate that the system provides reliable and consistent diagnostic predictions. Therefore, this expert system is expected to serve as a preliminary tool for the public to identify potential dental diseases before consulting with dental health professionals.