<![CDATA[Mental health disorders are a crucial public health issue in Indonesia, as reflected in the 2023 National Health Survey (SKI), which reported that 2.0% of the population aged ≥15 years old were diagnosed with mental health problems, and approximately 20% of the 250 million people experiencing mental health problems do not yet have access to adequate services. Although many previous studies have developed digital applications, such as “Serenity” and “CERDAS” in Indonesia for psychological assessment using instruments such as the DASS-21, these applications only provide general recommendations and do not provide personalized self-healing guidance. To address this gap, this study developed and tested “SERAYA”, a mobile application designed not only to assess mental health levels but also to provide self-healing recommendations. This application integrates two standard instruments, the DASS-21 and the PSS-10, to measure depression, anxiety, stress, and perceived stress. A rule-based expert system using forward chaining processes the assessment scores; for example, “IF DASS-21 depression score ≥ 28 THEN recommendation = ‘CBT Therapy’”. Based on this score, the system generates specific recommendations or direct referrals to mental health professionals for severe cases. SERAYA's functionality was verified through successful black-box testing. Initial usability assessments using the System Usability Scale (SUS) with 11 respondents yielded an average score of 80.68, indicating good usability and ease of learning for early users. While these initial results are encouraging, they are derived from a limited, non-clinical sample and cannot be generalized to the entire Indonesian population. Overall, this study demonstrates that “SERAYA” serves as a viable proof-of-concept for providing personalized early mental health support and illustrates the potential of rule-based systems in digital health applications. Future research should focus on larger-scale validation, clinical integration for professional referrals, and the application of machine learning techniques to enable dynamic and tailored personalization.]]>
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