An Intelligent Web-Based Mental Health Management Platform with Rule-Based Music Therapy Recommendation - Delimayanti, Mera Kartika; Wibowo, Harits Taqiy (Journal of Applied Informatics and Computing, 2025)
This research developed a web-based application for mental health management with an emotional music therapy recommendation feature using Rule-Based Filtering. The system is designed to help individuals recognize and manage emotional conditions caused by life pressures, work stress, and often overlooked psychological issues. A 2023 survey showed that 43% of respondents were concerned about mental health problems, followed by stress at 40%, while 43.8% of parents of teenagers managed their children’s mental health issues independently, 19.2% did not know where to seek help, and 15.4% believed the problems would improve on their own. The system analyzes daily emotional input and weekly PANAS questionnaires to classify moods (Positive, Negative, Mixed, Neutral) based on Positive Affect (PA) and Negative Affect (NA) scores, then recommends relevant music from the database. The technical implementation uses Laravel for the backend and Tailwind CSS for the frontend. Black Box Testing showed 100% functionality. User Acceptance Test (UAT) with 32 respondents resulted in UAT-J 90.25%, UAT-K 90.41%, UAT-R 89.18%, and UAT-A 91.24%. The System Usability Scale (SUS) reached an average score of 85 (very high), while the Net Promoter Score (NPS) was 59.37% (62.50% Promoters), indicating strong user satisfaction and loyalty. This research is expected to help individuals monitor emotional conditions and increase mental health awareness through an innovative music-based approach.