Teeth are important in the process of breaking down food, but many people do not pay attention to dental health. Unfortunately, most people especially in Indonesia are unconscious and don't really care about the dental health they experience. The main factor is the pattern of consumption of sweet foods and the high cost of consultation with the dentist. Therefore an expert system is needed which is expected to be a solution to diagnose dental disease. The present study applies the Naïve Bayes method because it is based on looking at the probabilities that exist in dental disease data through several perceived symptoms. The diagnosis process is carried out by inputting symptoms of the disease then the probability value of each class is calculated then the final result is the disease class that has the highest probability. The test results in this study involved testing data that were able to produce an accuracy of 93% so that the system could be applied to dental disease
                        
                        
                        
                        
                            
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