Application of Facial Expression Using YOLOv11 to Measure Interest in ‎Training Materials

  • Authors

    • I. Imran Department of Electrical Engineering, Hasanuddin University, Gowa, Indonesia
    • W Wardi Department of Informatics Engineering, Hasanuddin University, Gowa, Indonesia
    • Ingrid Nurtanio Department of Electrical Engineering, Hasanuddin University, Gowa, Indonesia
    • Faizal Arya Samman Department of Electrical Engineering, Hasanuddin University, Gowa, Indonesia
    https://doi.org/10.14419/vxgf1w29

    Received date: June 13, 2025

    Accepted date: July 16, 2025

    Published date: August 1, 2025

  • Facial Expression Recognition; YOLOv11; Interest Detection; Training Materials Evaluation; ‎Deep Learning in Education

  • Abstract

    The purpose of this study is to assess trainees' proficiency with the Convolutional Neural ‎Network (CNN) model in recognizing emotions from facial expressions. A wide range of ‎applications in training, education, and human-machine interaction could benefit greatly from ‎computer vision-based emotion recognition. A CNN model created especially to identify ‎important emotions including happiness, sadness, anger, and fear is trained and tested in this ‎study using a dataset of facial expressions. The test findings demonstrate that the CNN model ‎can accurately and efficiently classify emotions and offer valuable information about trainees' ‎strengths and limitations in identifying different emotions. This study emphasizes how CNN-‎based technology may be used to help assess and enhance emotion recognition skills in the ‎setting of professional training‎.

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  • How to Cite

    Imran, I. . ., Wardi, W. ., Nurtanio, I. ., & Samman, F. A. . (2025). Application of Facial Expression Using YOLOv11 to Measure Interest in ‎Training Materials. International Journal of Basic and Applied Sciences, 14(4), 6-16. https://doi.org/10.14419/vxgf1w29