Klasifikasi Emosi Pada Raut Wajah Pelajar Menggunakan Ekstraktor Fitur Face Mesh Dan Metode Support Vector Machine
Kata Kunci:
pengenalan emosi, facial landmark, hyperparameter tuning, SVM, confusion matrixAbstrak
Naskah ini akan diterbitkan di Konferensi Nasional SENTRIN
Referensi
Anon. 2022. Facial Landmark Detection with Mediapipe & Creating Animated Snapchat Filters. International Journal For Innovative Engineering and Management Research, pp.98–107. https://doi.org/10.48047/IJIEMR/V11/I06/10.
Bhatia, S., Tomar, U. and Jain, A.V., 2021. Comparing SVM and Neural Networks’ performance in Face Detection. In: 2021 International Conference on Intelligent Technologies (CONIT). [online] 2021 International Conference on Intelligent Technologies (CONIT). Hubli, India: IEEE. pp.1–7. https://doi.org/10.1109/CONIT51480.2021.9498383.
Gupta, A., D’Cunha, A., Awasthi, K. and Balasubramanian, V., 2022. DAiSEE: Towards User Engagement Recognition in the Wild. Available at: <http://arxiv.org/abs/1609.01885> [Accessed 8 August 2024].
Krithika L.B and Lakshmi Priya GG, 2016. Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric. Procedia Computer Science, 85, pp.767–776. https://doi.org/10.1016/j.procs.2016.05.264.
Lek, J.X.-Y. and Teo, J., 2023. Academic Emotion Classification Using FER: A Systematic Review. Human Behavior and Emerging Technologies, 2023, pp.1–27. https://doi.org/10.1155/2023/9790005.
Nalepa, J. and Kawulok, M., 2019. Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 52(2), pp.857–900. https://doi.org/10.1007/s10462-017-9611-1.
Nurrahma Rosanti Paidja, A. and Bachtiar, F.A., 2022. Engagement Emotion Classification through Facial Landmark Using Convolutional Neural Network. In: 2022 2nd International Conference on Information Technology and Education (ICIT&E). [online] 2022 2nd International Conference on Information Technology and Education (ICIT&E). Malang, Indonesia: IEEE. pp.234–239. https://doi.org/10.1109/ICITE54466.2022.9759546.
Patil, M. and Kagalkar, R., 2015. An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People. International Journal of Computer Applications, 118(3), pp.14–19. https://doi.org/10.5120/20725-3080.
Siam, A.I., Soliman, N.F., Algarni, A.D., Abd El-Samie, F.E. and Sedik, A., 2022. Deploying Machine Learning Techniques for Human Emotion Detection. Computational Intelligence and Neuroscience, 2022, pp.1–16. https://doi.org/10.1155/2022/8032673.
Solanki, N. and Mandal, S., 2022. Engagement Analysis Using DAiSEE Dataset. In: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). [online] 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). Singapore, Singapore: IEEE. pp.223–228. https://doi.org/10.1109/ICARCV57592.2022.10004250.
Sun, Y., Wong, A.K.C. and Kamel, M.S., 2009. CLASSIFICATION OF IMBALANCED DATA: A REVIEW. International Journal of Pattern Recognition and Artificial Intelligence, 23(04), pp.687–719. https://doi.org/10.1142/S0218001409007326.
Thuseethan, S., Rajasegarar, S. and Yearwood, J., 2019. Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN. In: Q. Yang, Z.-H. Zhou, Z. Gong, M.-L. Zhang and S.-J. Huang, eds. Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science. [online] Cham: Springer International Publishing. pp.387–399. https://doi.org/10.1007/978-3-030-16142-2_30.
Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A. and Movellan, J.R., 2014. The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing, 5(1), pp.86–98. https://doi.org/10.1109/TAFFC.2014.2316163.
Anon. 2022. Facial Landmark Detection with Mediapipe & Creating Animated Snapchat Filters. International Journal For Innovative Engineering and Management Research, pp.98–107. https://doi.org/10.48047/IJIEMR/V11/I06/10.
Bhatia, S., Tomar, U. and Jain, A.V., 2021. Comparing SVM and Neural Networks’ performance in Face Detection. In: 2021 International Conference on Intelligent Technologies (CONIT). [online] 2021 International Conference on Intelligent Technologies (CONIT). Hubli, India: IEEE. pp.1–7. https://doi.org/10.1109/CONIT51480.2021.9498383.
Gupta, A., D’Cunha, A., Awasthi, K. and Balasubramanian, V., 2022. DAiSEE: Towards User Engagement Recognition in the Wild. Available at: <http://arxiv.org/abs/1609.01885> [Accessed 8 August 2024].
Krithika L.B and Lakshmi Priya GG, 2016. Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric. Procedia Computer Science, 85, pp.767–776. https://doi.org/10.1016/j.procs.2016.05.264.
Lek, J.X.-Y. and Teo, J., 2023. Academic Emotion Classification Using FER: A Systematic Review. Human Behavior and Emerging Technologies, 2023, pp.1–27. https://doi.org/10.1155/2023/9790005.
Nalepa, J. and Kawulok, M., 2019. Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 52(2), pp.857–900. https://doi.org/10.1007/s10462-017-9611-1.
Nurrahma Rosanti Paidja, A. and Bachtiar, F.A., 2022. Engagement Emotion Classification through Facial Landmark Using Convolutional Neural Network. In: 2022 2nd International Conference on Information Technology and Education (ICIT&E). [online] 2022 2nd International Conference on Information Technology and Education (ICIT&E). Malang, Indonesia: IEEE. pp.234–239. https://doi.org/10.1109/ICITE54466.2022.9759546.
Patil, M. and Kagalkar, R., 2015. An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People. International Journal of Computer Applications, 118(3), pp.14–19. https://doi.org/10.5120/20725-3080.
Siam, A.I., Soliman, N.F., Algarni, A.D., Abd El-Samie, F.E. and Sedik, A., 2022. Deploying Machine Learning Techniques for Human Emotion Detection. Computational Intelligence and Neuroscience, 2022, pp.1–16. https://doi.org/10.1155/2022/8032673.
Solanki, N. and Mandal, S., 2022. Engagement Analysis Using DAiSEE Dataset. In: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). [online] 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). Singapore, Singapore: IEEE. pp.223–228. https://doi.org/10.1109/ICARCV57592.2022.10004250.
Sun, Y., Wong, A.K.C. and Kamel, M.S., 2009. CLASSIFICATION OF IMBALANCED DATA: A REVIEW. International Journal of Pattern Recognition and Artificial Intelligence, 23(04), pp.687–719. https://doi.org/10.1142/S0218001409007326.
Thuseethan, S., Rajasegarar, S. and Yearwood, J., 2019. Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN. In: Q. Yang, Z.-H. Zhou, Z. Gong, M.-L. Zhang and S.-J. Huang, eds. Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science. [online] Cham: Springer International Publishing. pp.387–399. https://doi.org/10.1007/978-3-030-16142-2_30.
Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A. and Movellan, J.R., 2014. The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing, 5(1), pp.86–98. https://doi.org/10.1109/TAFFC.2014.2316163.
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