<![CDATA[Tourette Syndrome (TS) is a neurological disorder affecting children under 18, with an estimated prevalence of over 150,000 cases annually in Indonesia and 1% globally. Misdiagnosis rates of 20-30% complicate effective management. TS involves involuntary, repetitive tics, ranging from sudden movements or sounds to aggressive behaviors, posing significant challenges for diagnosis and treatment. This study utilizes an expert system with a case-based reasoning (CBR) approach to improve TS diagnosis. Interviews with TS patients and specialists provided data on symptoms and diagnostic structures. A weighting mechanism and an accumulation formula were implemented to deliver accurate diagnostic outcomes and first aid suggestions, optimized for minimal computing resources without reliance on extensive datasets. Testing on 10 patients, under expert supervision, demonstrated the system's ability to accurately diagnose and classify TS. The system effectively simulates expert-level TS detection, offering precise diagnosis and recommendations, potentially enhancing early intervention and reducing diagnostic errors.]]>
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