<![CDATA[Anxiety disorders are a form of mental disorders that often occur and have a significant impact on the quality of life of individuals. However, the process of diagnosing this disorder still faces various challenges, especially limited access to professionals and difficulties in identifying the type of disorder based on varying symptoms. This research aims to design and implement an expert-based system to help the early diagnosis process of anxiety disorders quickly and accurately. The system was developed as a web application that allows users to answer a series of questions related to the symptoms experienced, then provide possible types of disorders based on the calculation of confidence levels. The method used is forward chaining as an inference engine to conduct a rule and certainty factor search to calculate the level of confidence in the identification results of the symptoms experienced by the user. Data collected from the literature and interviews with experts were built into a knowledge base consisting of 8 types of anxiety disorders with a total of 41 symptoms. Each rule in the system is formulated using an if-then structure that combines CF values to represent the level of confidence in the symptoms and the results of logical inference with advanced tracking methods. The system was tested using 20 test data in the form of symptom-based case simulations. The results of the evaluation showed that the system was able to produce an initial diagnosis with an accuracy rate of up to 100% based on comparison with manual diagnosis from experts. This system also provides explanatory information in the form of confidence level in each diagnosis result. These findings suggest that the Certainty Factor and Forward Chaining approaches are effective in building expert systems for diagnosing anxiety disorders and have the potential to be further developed as a screening tool in educational or primary health care settings.]]>
