Emotional Detection of Students During The Learning Process Using Yolo V8
DOI:
https://doi.org/10.58982/krisnadana.v5i2.1061Keywords:
Emotional Detection; YOLOv8; Facial Expression; Computer Vision; Deep LearningAbstract
This research develops an emotional detection system for students during learning using YOLOv8 model N and Computer Vision technology. The main objective measures accuracy of facial expression recognition across happy, sad (murung), neutral (normal), and confused classes while evaluating lighting and camera position impacts. Dataset of 1500 Kaggle images was labeled on Roboflow with augmentations including flip, crop, blur, and noise, then trained on Google Colab for 100 epochs using pre-trained YOLOv8n weights. Model validation employed confusion matrices, precision-recall curves, and real-time webcam testing on 10 students at 5-15 cm distances. Key findings show accuracies of 75% (happy), 62% (sad), 69% (neutral), and 33% (confused), averaging 59.75% under >39 lux frontal lighting. Optimal performance occurred at 0°-10° angles, but backlit conditions reduced efficacy by 25-35% due to shadow occlusion. The system enables real-time classroom monitoring with 4.2 ms inference latency, supporting cognitive engagement assessment despite bingung class limitations from dataset imbalance. Future improvements recommend expanded ambiguous samples and lighting augmentations.
References
P. Ross and K. Maynard, “Towards a 4th industrial revolution,” Intelligent Buildings International, vol. 13, no. 3. Taylor & Francis, pp. 159–161, 2021. doi: https://doi.org/10.1080/17508975.2021.1873625.
A. K. Sahai and N. Rath, “Artificial intelligence and the 4th industrial revolution,” in Artificial intelligence and machine learning in business management, CRC Press, 2021, pp. 127–143.
J. Martínez and J. Vega, “ROS system facial emotion detection using machine learning for a low-cost robot based on Raspberry Pi,” Electronics, vol. 12, no. 1, p. 90, 2022, doi: https://doi.org/10.3390/electronics12010090.
M. N. Ab Wahab, A. Nazir, A. T. Z. Ren, M. H. M. Noor, M. F. Akbar, and A. S. A. Mohamed, “Efficientnet-lite and hybrid CNN-KNN implementation for facial expression recognition on raspberry pi,” IEEE Access, vol. 9, pp. 134065–134080, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3113337.
K. Anusudha, “Real time face recognition system based on YOLO and InsightFace,” Multimed. Tools Appl., vol. 83, no. 11, pp. 31893–31910, 2024, doi: https://doi.org/10.1007/s11042-023-16831-7.
D. Garg, P. Goel, S. Pandya, A. Ganatra, and K. Kotecha, “A deep learning approach for face detection using YOLO,” in 2018 IEEE Punecon, 2018, pp. 1–4. doi: https://doi.org/10.1109/PUNECON.2018.8745376.
E. K. Varnima and C. Ramachandran, “Real-time Gender Identification from Face Images using you only look once (yolo),” in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020, pp. 1074–1077. doi: https://doi.org/10.1109/ICOEI48184.2020.9142989.
A. P. Saputri, A. Taqwa, and S. Soim, “Analisis Deteksi Objek Citra Digital Menggunakan Algoritma Yolo dan CNN Dengan Arsitektur REPVGG pada Sistem Pendeteksian dan Pengenalan Ekspresi Wajah,” Syntax Lit. J. Ilm. Indones., vol. 7, no. 9, pp. 13068–13080, 2022, doi: https://doi.org/10.36418/syntax-literate.v7i9.9393.
A. T. Khalaf and S. K. Abdulateef, “Multi-Class of Retinal Diseases Classification via Deep Learning Techniques Based on Fundus Images,” Samarra J. Pure Appl. Sci., vol. 6, no. 3, pp. 274–286, 2024, doi: https://doi.org/10.54153/sjpas.2024.v6i3.881.
A. T. Khalaf and S. K. Abdulateef, “Ophthalmic diseases classification based on YOLOv8,” J. Robot. Control, vol. 5, no. 2, pp. 408–415, 2024, doi: https://doi.org/10.18196/jrc.v5i2.21208.
S. A. Hussain and A. S. A. Al Balushi, “A real time face emotion classification and recognition using deep learning model,” in Journal of physics: Conference series, 2020, vol. 1432, no. 1, p. 12087. doi: 10.1088/1742-6596/1432/1/012087.
I. P. A. E. D. Udayana, N. P. E. Kherismawati, and I. G. I. Sudipa, “Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic,” Telemat. J. Inform. dan Teknol. Inf., vol. 19, no. 2, pp. 229–244, 2022.
F. R. T. Ferreira, L. M. do Couto, and G. de Melo Baptista Domingues, “Exploring the potential of YOLOv8 in hybrid models for facial mask identification in diverse environments,” Neural Comput. Appl., vol. 36, no. 35, pp. 22037–22052, 2024, doi: https://doi.org/10.1007/s00521-024-10351-7.
C. Dewi, D. Manongga, Hendry, E. Mailoa, and K. D. Hartomo, “Deep learning and YOLOv8 utilized in an accurate face mask detection system,” Big Data Cogn. Comput., vol. 8, no. 1, p. 9, 2024, doi: https://doi.org/10.3390/bdcc8010009.
P. Kanani and M. Padole, “Deep learning to detect skin cancer using google colab,” Int. J. Eng. Adv. Technol. Regul. Issue, vol. 8, no. 6, pp. 2176–2183, 2019, doi: https://doi.org/10.35940/ijeat.F8587.088619.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ni Luh Wiwik Sri Rahayu Ginantra, Suyud Setiawan Al Arif, Ni Wayan Jeri Kusuma Dewi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
