<![CDATA[Mental health issues are increasingly recognized as a global challenge, affecting more than 450 million people worldwide, with a significant treatment gap in developing countries such as Indonesia. The limited number of mental health professionals highlights the need for alternative solutions to support early diagnosis. This study aims to design and implement an intelligent expert system for the diagnosis of mental disorders using the Certainty Factor (CF) method. The CF approach was selected for its ability to handle uncertainty and subjectivity in expert reasoning, particularly in cases where symptoms overlap across different disorders. The research methodology includes problem analysis, data collection, system design, implementation, and evaluation. The system was tested using a dataset of mental disorder symptoms, including depression, anxiety, schizophrenia, and bipolar disorder. The results indicate that the system can diagnose bipolar disorder with the highest CF value (0.951), followed by depression (0.883), anxiety disorder (0.853), and schizophrenia (0.510). These findings demonstrate that the CF-based system can provide accurate and realistic initial diagnoses that approximate expert judgment. This research contributes to the field of health informatics by providing a decision-support tool that can be integrated into telehealth platforms, enabling communities to gain faster access to mental health screening.]]>
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