<![CDATA[A Public awareness of the importance of maintaining dental and oral health remains relatively low, even though disorders in this area can lead to serious complications such as infections, abscesses, and systemic diseases. On the other hand, limited access to dental health services due to uneven distribution of medical personnel and high consultation costs presents a significant challenge. To address this issue, this study developed an expert system for diagnosing dental diseases using the Naive Bayes method. This method applies a probabilistic classification approach to predict disease based on observed symptoms. Data were obtained through interviews with dentists and collection of real case data at Puskesmas Imbanagara. The system was tested using 50 patient cases, with results showing 48 diagnoses matched the expert's opinion, achieving an accuracy rate of 96%. These results demonstrate that the Naive Bayes method is effective for early-stage dental disease diagnosis. The system offers a practical solution to assist the public in recognizing symptoms independently before seeking professional consultation.]]>
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