<![CDATA[Otitis, especially otitis media, otitis externa, and otitis interna, is a common health issue in humans. Accurate identification and diagnosis are crucial for effective treatment. Expert systems have become an effective solution in this field as they can leverage medical knowledge to support decision-making processes. This research aims to develop an Expert System for Otitis Disease Detection using three different methods: Certainty Factor (CF), Bayes' Theorem, and Dempster-Shafer Method. The CF method is used to measure the level of confidence in decision-making, Bayes' Theorem utilizes probabilities to support diagnosis, and Dempster-Shafer matches previous cases with current symptoms to provide diagnostic recommendations. In this study, otitis symptom data were collected from previously diagnosed patients to train the expert system. The system was then tested with new cases to analyze the performance and accuracy of each method. The research results indicate that all three methods have the potential to detect otitis disease with varying levels of accuracy. In some situations, one method may outperform the others, suggesting that using a combination or integration of methods can enhance diagnostic accuracy. This research makes a significant contribution to the development of expert systems in the healthcare and medical services field. It is expected to assist doctors in the faster and more accurate diagnosis of otitis diseases. The calculation results show that the Certainty Factor method has the highest confidence level in diagnosing otitis media (86%), otitis externa (79%), and otitis interna (87%). While Dempster-Shafer has lower confidence levels in all cases, it still provides a significant contribution in certain situations. ]]>
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