Oral and dental health is an integral part of general health, yet high treatment costs and limited availability of dentists, especially in non-urban areas, often lead the public to delay examinations until complaints worsen. This research aims to develop a web-based expert system capable of providing early indications of oral and dental diseases quickly, accurately, and accessibly through a self-screening mechanism. This web application provides an expert system developed using Python (Flask) and designed with login/logout features for data security, as well as storing user information and diagnosis results in a database. The system utilizes the Forward Chaining method as the primary inference engine to process user-inputted symptoms, and integrates Certainty Factor to calculate and determine the confidence level of the diagnosis results. Testing has demonstrated ease of use, where users only need to answer symptom questions with "yes" or "no" to obtain a disease diagnosis and relevant treatment suggestions. Performance analysis based on trials with 40 simulated data shows the system achieved an accuracy rate of 85%, making it a reliable tool for initial screening.
                        
                        
                        
                        
                            
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