Telecounseling Media Based on Facial Expression Analysis as a Tool for Detecting Students' Suicidal Tendencies
DOI:
https://doi.org/10.54956/edukasi.v12i2.634Keywords:
Telecounseling Media, Facial Analysis, Detection Tool, SuicideAbstract
This research aims to assess the implementation and effectiveness of telecounseling media based on facial expression analysis in detecting suicidal tendencies in students. The method used is a quantitative research design. This research will measure the level of effectiveness through assessments from experts and data analysis using statistical methods. The results of implementation using pilot tests three times showed that 2 out of 3 counsellors had moderate suicidal tendencies, and effectiveness tests by Informatics and Psychology experts using the Cohen's Kappa method showed that this telecounseling media was effective in capturing facial expressions during counselling sessions as well as trouble graphs. FEA is accurate in describing the client's emotional condition.
References
Brown, A., & Green, T. (2018). Beyond teaching instructional design models: exploring the design process to advance professional development and expertise. Journal of Computing in Higher Education, 30(1). https://doi.org/10.1007/s12528-017-9164-y
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
Daulay, N., Harahap, A., & Sinaga, M. (2024). Online Counseling Online counselling-based information services: How can it reduce students’ academic stress? Persona:Jurnal Psikologi Indonesia, 12(2), 169–181. https://doi.org/10.30996/persona.v12i2.8117
Feeny, A., Sousa, F., & Wessel, P. (2024). University and college counselling services in Scotland: One‐on‐one counselling amid the student mental health crisis. Counselling and Psychotherapy Research. https://doi.org/10.1002/capr.12858
Garcia, H., Rodriguez, P., & Zhang, Y. (2023). Facial expression analysis in mental health: Opportunities and challenges. Computers in Human Behavior, 131, 107–115. https://doi.org/10.1016/j.chb.2022.107115
Khan, A., Gupta, V., & Martinez, J. (2019). Prevalence and predictors of suicide risk among college students. Journal of Affective Disorders, 259, 431–439. https://doi.org/10.1016/j.jad.2019.08.053
Lee, C., Smith, D., & White, J. (2020). Facial expression analysis for mental health: A review of current applications. International Journal of Mental Health and Addiction, 18(3), 672–689. https://doi.org/10.1007/s11469-019-00133-9
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282. https://doi.org/10.11613/BM.2012.031
Schlegel, K., Scherer, K. R., & Kreibig, S. D. (2021). Facial expression analysis for emotion detection in mental health. Emotion, 21(2), 282–293. https://doi.org/10.1037/emo0000731
Smith, A., & Jones, R. (2021). Innovations in telecounseling: A review of emerging technologies. Journal of Behavioral Health Services & Research, 48(1), 45–58. https://doi.org/10.1007/s11414-020-09698-3
Wilson, E., & Thompson, S. (2021). Utilizing facial expression analysis in telehealth: A systematic review. Telemedicine and E-Health, 27(8), 755–765. https://doi.org/10.1089/tmj.2020.0305
