Implementation of YOLO Algorithm in Adolescent Suicide Ideation Monitoring System Based on Real-Time Data Analysis
DOI:
https://doi.org/10.56988/chiprof.v4i1.87Keywords:
Adolescent, Algorithm YOLO, Artificial intelligence , Monitoring, Suicidal IdeationAbstract
This study aims to develop and implement a suicide ideation monitoring system in adolescents based on the YOLO (You Only Look Once) algorithm with real-time data analysis. The YOLO algorithm is used to detect facial expressions that reflect negative emotions, such as sadness and anxiety, which can be early indicators of suicidal ideation. The research methods used are qualitative and quantitative approaches, including the collection of facial image data, model training using the TensorFlow and OpenCV frameworks, and testing the system's performance in detecting facial expressions in real time. The system test is carried out by comparing the results of YOLO detection against reference data to measure the accuracy and speed of detection. The results of the study show that the developed system is able to detect facial expressions with an accuracy rate of 92% and an average detection speed of 30 milliseconds per frame. In addition, the system can be integrated with communication platforms to provide warning notifications to related parties as a form of early intervention. Thus, this study proves that the YOLO algorithm is effective in developing a suicide ideation monitoring system based on real-time data analysis so that it can be a preventive solution in supporting adolescent mental health.
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