Design of A Face Recognition Application for Adjustment of Relaxation Audio in Psychological First Aid Services by Volunteers in Disaster Situations - Elsy Maharani Putri; Muhammad Adri; Dedy Irfan; Geovanne Farell (Universitas Negeri Padang, 2026)
This study aims to develop a PsyAssist application based on face recognition to help disaster volunteers detect survivors' emotions in real-time through facial expression analysis using the YOLO algorithm that recommends personalized relaxation audio. This application is developed with the Flutter framework, Flask API python and the Waterfall model. The main features include: (1) facial expression-based emotion detection, (2) relaxation audio recommendations according to emotional conditions, and (3) audio satisfaction graph visualization based on user responses. The model training process was carried out for 100 epochs, with results showing an increase in accuracy as the number of epochs increases. At the 100th epoch, the model achieved a precision value of 0.8 and a recall matrix of 0.85, indicating quite good performance in detecting facial expressions. The existence of this application is expected to increasing the technological support needed by PFA volunteers in disaster situations