Implementasi Metode Teorema Bayes Pada Diagnosa Penyakit Gigi

Abstract

This study implements the Bayes Theorem method to diagnose dental diseases based on patient-reported symptoms. Bayes Theorem operates by calculating the probability of a disease as a hypothesis based on available evidence in the form of observed symptoms. In this study, patient-reported symptoms are analyzed probabilistically to diagnose several types of dental diseases, namely gingivitis, dental caries, periodontal abscess, and pulpitis. The system utilizes conditional probability values between symptoms and diseases obtained from expert knowledge to calculate the posterior probability of each disease. The system is developed using the Python programming language and consists of a knowledge base containing symptom data, disease types, and probability values, as well as an inference engine that applies Bayes Theorem calculations. Research data were collected through interviews with dentists at Dian Farma Clinic, South Jayapura. The results indicate that the Bayes Theorem method is effective in supporting the early diagnosis of dental diseases in an objective and measurable manner; however, the diagnostic results still require further confirmation by professional medical personnel.