Analisa Suasana Belajar Kelas Berdasarkan Deteksi Ekspresi Wajah Menggunakan Deepface
DOI:
https://doi.org/10.26418/jp.v10i3.85462Keywords:
Emosi, Ekspresi Wajah, Deepface-MTCNN, Deepface-Retinaface, Suasana BelajarAbstract
Emosi peserta kelas pada pelajaran merupakan salah satu kunci keberhasilan proses belajar mengajar. Perasaan senang dan bisa memahami merupakan hal positif dalam belajar. Sebaliknya perasaan bosan dan tidak tertarik merupakan hal negatif yang dapat menurunkan produktifitas belajar. Emosi yang terjadi pada seseorang dapat terlihat pada ekspresi wajahnya. Demikian halnya pada peserta pelajaran di kelas yang dalam penelitian ini menggunakan objek mahasiswa. Dengan menggunakan metode Deepface-MTCNN dan Deepface-Retinaface dapat dikenali emosi setiap peserta kelas dan menentukan suasana belajar. Dipadukan dengan hasil survey peserta kelas, didapatkan adanya hubungan antara suasana belajar yang dirasakan mahasiswa saat mengikuti kuliah di kelas dengan hasil deteksi ekspresi wajah. Saat peserta kelas merasakan suasana menyenangkan, bisa memahami, sulit memahami, atau tidak menarik terjadi pula perubahan pada ekspresi wajah. Berdasarkan pengujian akurasi deteksi Deepface-Retinaface sebesar 86% dan Deepface-MTCNN sebesar 91%. Untuk analisa korelasi metode Deepface-MTCNN memiliki hasil lebih baik karena memiliki korelasi kuat yang lebih banyak. sebanyak 4 korelasi positif dengan nilai lebih besar dari 0,5 dan 5 korelasi negatif dengan nilai lebih kecil dari -0,5.
References
F. Prasetyo and D. Dasari, “Studi Literatur: Identifikasi Kecemasan Matematika dan Motivasi Belajar Terhadap Hasil Belajar Matematika Siswa,†RANGE: Jurnal Pendidikan Matematika, vol. 4, no. 2, pp. 240–253, Jan. 2023, doi: 10.32938/JPM.V4I2.3649.
I. G. P. S. Wijaya, A. A. Firdaus, A. P. J. Dwitama, and M. Mustiari, “Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC),†Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 5, pp. 559–566, Oct. 2018, doi: 10.25126/jtiik20185935.
W. Maqableh, F. Y. Alzyoud, and J. Zraqou, “The use of facial expressions in measuring students’ interaction with distance learning environments during the COVID-19 crisis,†Visual Informatics, vol. 7, no. 1, pp. 1–17, Mar. 2023, doi: 10.1016/J.VISINF.2022.10.001.
F. A. Bachtiar and M. Wafi, “Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark,†Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 5, pp. 949–956, Oct. 2021, doi: 10.25126/jtiik.202184434.
S. Gu, F. Wang, N. P. Patel, J. A. Bourgeois, and J. H. Huang, “A model for basic emotions using observations of behavior in Drosophila,†Front Psychol, vol. 10, no. APR, p. 445286, Apr. 2019, doi: 10.3389/FPSYG.2019.00781/BIBTEX.
R. Karbauskaite, L. Sakalauskas, and G. Dzemyda, “Kriging Predictor for Facial Emotion Recognition Using Numerical Proximities of Human Emotions,†Informatica (Netherlands), vol. 31, no. 2, 2020, doi: 10.15388/20-INFOR419.
A. Semeraro, S. Vilella, and G. Ruffo, “PyPlutchik: Visualising and comparing emotion-annotated corpora,†PLoS One, vol. 16, no. 9, p. e0256503, Sep. 2021, doi: 10.1371/JOURNAL.PONE.0256503.
J. Pordoy et al., “Multi-Frame Transfer Learning Framework for Facial Emotion Recognition in e-Learning Contexts,†IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3478072.
B. Fang, X. Li, G. Han, and J. He, “Facial Expression Recognition in Educational Research from the Perspective of Machine Learning: A Systematic Review,†IEEE Access, vol. 11, pp. 112060–112074, 2023, doi: 10.1109/ACCESS.2023.3322454.
J. X. Y. Lek and J. Teo, “Academic Emotion Classification Using FER: A Systematic Review,†Hum Behav Emerg Technol, vol. 2023, no. 1, p. 9790005, Jan. 2023, doi: 10.1155/2023/9790005.
I. Saadi, D. W. cunningham, A. Taleb-Ahmed, A. Hadid, and Y. El Hillali, “Driver’s facial expression recognition: A comprehensive survey,†Expert Syst Appl, vol. 242, p. 122784, May 2024, doi: 10.1016/J.ESWA.2023.122784.
P. E. Wajah et al., “Pengenalan Ekspresi Wajah Pengemudi Berbasis Fitur Eigenface dan Gray Level Co-Occurance Matrice,†JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 9, no. 2, pp. 277–281, Aug. 2023, Accessed: Sep. 14, 2024. [Online]. Available: https://jurnal.untan.ac.id/index.php/jepin/article/view/61857
S. Li and W. Deng, “Deep Facial Expression Recognition: A Survey,†IEEE Trans Affect Comput, vol. 13, no. 3, pp. 1195–1215, 2022, doi: 10.1109/TAFFC.2020.2981446.
S. K. Khare, V. Blanes-Vidal, E. S. Nadimi, and U. R. Acharya, “Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations,†Information Fusion, vol. 102, p. 102019, Feb. 2024, doi: 10.1016/J.INFFUS.2023.102019.
E. Owusu, J. K. Appati, and P. Okae, “Robust facial expression recognition system in higher poses,†Vis Comput Ind Biomed Art, vol. 5, no. 1, pp. 1–15, Dec. 2022, doi: 10.1186/S42492-022-00109-0/FIGURES/19.
K. S. Jaswanth and D. S. David, “A Novel Based 3D Facial Expression Detection Using Recurrent Neural Network,†2020 International Conference on System, Computation, Automation and Networking, ICSCAN 2020, Jul. 2020, doi: 10.1109/ICSCAN49426.2020.9262287.
R. Alroobaea, “Sentiment Analysis on Amazon Product Reviews using the Recurrent Neural Network (RNN),†International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 314–318, 2022, doi: 10.14569/IJACSA.2022.0130437.
H. A. Bouarara, “Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media,†https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSSCI.2021070101, vol. 13, no. 3, pp. 1–11, Jan. 1AD, doi: 10.4018/IJSSCI.2021070101.
S. P. Yadav, S. Zaidi, A. Mishra, and V. Yadav, “Survey on Machine Learning in Speech Emotion Recognition and Vision Systems Using a Recurrent Neural Network (RNN),†Archives of Computational Methods in Engineering, vol. 29, no. 3, pp. 1753–1770, May 2022, doi: 10.1007/S11831-021-09647-X/METRICS.
N. Samadiani et al., “A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data,†Sensors 2019, Vol. 19, Page 1863, vol. 19, no. 8, p. 1863, Apr. 2019, doi: 10.3390/S19081863.
Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,†IEEE Trans Cybern, vol. 50, no. 9, pp. 3840–3854, Sep. 2020, doi: 10.1109/TCYB.2020.2983860.
W. Zhou, H. Wang, and Z. Wan, “Ore Image Classification Based on Improved CNN,†Computers and Electrical Engineering, vol. 99, p. 107819, Apr. 2022, doi: 10.1016/J.COMPELECENG.2022.107819.
S. I. Serengil and A. Ozpinar, “Deep Facial Expression Recognition: A Survey,†IEEE Trans Affect Comput, vol. 13, no. 3, pp. 1195–1215, Apr. 2018, doi: 10.1109/TAFFC.2020.2981446.
O. Mohamad Nezami, M. Dras, L. Hamey, D. Richards, S. Wan, and C. Paris, “Automatic Recognition of Student Engagement Using Deep Learning and Facial Expression,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11908 LNAI, pp. 273–289, 2020, doi: 10.1007/978-3-030-46133-1_17/TABLES/8.
W. Wang, K. Xu, H. Niu, and X. Miao, “Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation,†Complexity, vol. 2020, 2020, doi: 10.1155/2020/4065207.
J. Pei and P. Shan, “A Micro-expression Recognition Algorithm for Students in Classroom Learning Based on Convolutional Neural Network.,†Traitement du Signal, vol. 36, no. 6, pp. 557–563, 2019, doi: https://doi.org/10.18280/ts.360611.
G. Tonguç and B. Ozaydın Ozkara, “Automatic recognition of student emotions from facial expressions during a lecture,†Comput Educ, vol. 148, p. 103797, Apr. 2020, doi: 10.1016/J.COMPEDU.2019.103797.
D. Dukić and A. S. Krzic, “Real-Time Facial Expression Recognition Using Deep Learning with Application in the Active Classroom Environment,†Electronics 2022, Vol. 11, Page 1240, vol. 11, no. 8, p. 1240, Apr. 2022, doi: 10.3390/ELECTRONICS11081240.
Ş. İ. SERENGİL and A. Özpinar, “A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules,†Journal of Information Technologies, vol. 17, no. 2, pp. 95–107, Apr. 2024, doi: 10.17671/GAZIBTD.1399077.
M. Wang and W. Deng, “Deep face recognition: A survey,†Neurocomputing, vol. 429, pp. 215–244, Mar. 2021, doi: 10.1016/J.NEUCOM.2020.10.081.
