<![CDATA[Depression is increasingly experienced by final-year university students due to academic pressure, with symptoms such as prolonged sadness, loss of motivation, sleep disturbances, and difficulty concentrating. Early detection remains limited because of restricted access to mental health professionals and the high cost of consultations. This study aims to develop an Android-based expert system for diagnosing depression levels using the Certainty Factor (CF) method. The research employed the Research and Development (R&D) method with the Rapid Application Development (RAD) model, which consists of requirements planning, design, construction, and implementation stages. The system utilizes 15 symptoms and classifies four levels of depression: mood disorder, mild depression, moderate depression, and severe depression. The evaluation was conducted on 20 respondents by comparing the system’s diagnostic results with expert analysis. The evaluation results showed that 17 out of 20 system diagnoses were consistent with the expert’s analysis, resulting in an accuracy rate of 85%. An example of the calculation process using the CF method produced a diagnostic value of 0.95 (95%), which falls into the severe depression category. The developed system is capable of supporting early detection of depression in a faster, more practical, and easily accessible manner through Android devices, and it can serve as an initial consultation tool for final-year students. The system can assist early detection more efficiently and with greater accessibility.]]>
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