Emotion Recognition Untuk Mendukung Kegiatan Pembelajaran Yang Menyenangkan
Studi Literatur
DOI:
https://doi.org/10.58520/jddat.v3i2.63Keywords:
Emotion recognition, Pendidikan yang menyenangkan, CNN, SVMAbstract
Perkembangan pendidikan setiap generasi memberikan tantangan tantangan yang cukup signifikan yang harus dihadapi oleh guru guru. Salah satu isu adalah pentingnya tingkat kesenangan ataupun pandangan positif siswa terhadap proses pembelajaran yang dilakukan. Lingkungan pendidikan sudah pernah menggunakan teknologi biometrik sebagai bagian dari sistem pendukung nya. Dalam penelitian ini kami melakukan tinjauan pustaka terkait penggunaan teknologi biometrik spesifiknya teknologi emotion recognition untuk membantu guru. Dari 225 artikel yang didapatkan dan setelah melewati proses analisis dasar dan konten, kami menemukan bahwa teknologi emotion recognition sudah bisa dikembangkan dengan menggunakan algoritma CNN maupun SVM dengan menggunakan dataset yang tersedia secara publik. Namun perlu adanya studi lebih mendalam, khususnya dalam penerapan di kelas formal dan penerimaan guru dan siswa terhadap teknologi ini.
References
B. Barhate and K. M. Dirani, “Career aspirations of generation Z: a systematic literature review,” European Journal of Training and Development, vol. 46, no. 1–2. Emerald Group Holdings Ltd., pp. 139–157, Jan. 24, 2022. doi: 10.1108/EJTD-07-2020-0124.
M. Hernandez-de-Menendez, C. A. Escobar Díaz, and R. Morales-Menendez, “Educational experiences with Generation Z,” International Journal on Interactive Design and Manufacturing, vol. 14, no. 3, pp. 847–859, Sep. 2020, doi: 10.1007/s12008-020-00674-9.
Z. Xiuwen and A. B. Razali, “An Overview of the Utilization of TikTok to Improve Oral English Communication Competence among EFL Undergraduate Students,” Universal Journal of Educational Research, vol. 9, no. 7, pp. 1439–1451, Jul. 2021, doi: 10.13189/ujer.2021.090710.
K. Gabrielova and A. A. Buchko, “Here comes Generation Z: Millennials as managers,” Bus Horiz, vol. 64, no. 4, pp. 489–499, Jul. 2021, doi: 10.1016/j.bushor.2021.02.013.
S. Obergriesser and H. Stoeger, “Students’ emotions of enjoyment and boredom and their use of cognitive learning strategies – How do they affect one another?,” Learn Instr, vol. 66, Apr. 2020, doi: 10.1016/j.learninstruc.2019.101285.
T. Inada, “Levels of Enjoyment in Class Are Closely Related to Improved English Proficiency,” English Language Teaching, vol. 15, no. 5, p. 69, Apr. 2022, doi: 10.5539/elt.v15n5p69.
E. Bensalem, “impacto del disfrute y la ansiedad en la voluntad de comunicarse de los estudiantes del idioma inglés,” Vivat Academia. Revista de Comunicación, pp. 91–111, Jan. 2022, doi: 10.15178/va.2022.155.e1310.
S. Bussa, A. Mani, S. Bharuka, and S. Kaushik, “Smart Attendance System using OPENCV based on Facial Recognition,” International Journal of Engineering Research and, vol. V9, no. 03, pp. 54–59, 2020, doi: 10.17577/ijertv9is030122.
M. Geetha, R. S. Latha, S. K. Nivetha, S. Hariprasath, S. Gowtham, and C. S. Deepak, “Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms,” in 2021 International Conference on Computer Communication and Informatics, ICCCI 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021. doi: 10.1109/ICCCI50826.2021.9402553.
H. M. Won, H. Lee, G. Song, Y. Kim, and N. Kwak, “Reliable Data Collection Methodology for Face Recognition in Preschool Children,” Sensors, vol. 22, no. 15, Aug. 2022, doi: 10.3390/s22155842.
L. Chen, S. Y. Yoon, C. W. Leong, M. Martin, and M. Ma, “An initial analysis of structured video interviews by using multimodal emotion detection,” in ERM4HCI 2014 - Proceedings of the 2nd ACM International Workshop on Emotion Representations and Modelling in Human-Computer Interaction Systems, Co-located with ICMI 2014, Association for Computing Machinery, Nov. 2014, pp. 1–6. doi: 10.1145/2668056.2668057.
M. Bani et al., “Behind the Mask: Emotion Recognition in Healthcare Students,” Med Sci Educ, vol. 31, no. 4, pp. 1273–1277, Aug. 2021, doi: 10.1007/s40670-021-01317-8.
N. Hamelin, O. El Moujahid, and P. Thaichon, “Emotion and advertising effectiveness: A novel facial expression analysis approach,” Journal of Retailing and Consumer Services, vol. 36, pp. 103–111, May 2017, doi: 10.1016/j.jretconser.2017.01.001.
C. B. Mpungose and S. B. Khoza, “Students’ Reflections on the Use of the Zoom Video Conferencing Technology for Online Learning at a South African University,” International Journal of African Higher Education, vol. 8, no. 1, pp. 159–178, Apr. 2021, doi: 10.6017/ijahe.v8i1.13371.
E. Susilawati, H. Lubis, S. Kesuma, and I. Pratama, “Antecedents of Student Character in Higher Education: The role of the Automated Short Essay Scoring (ASES) digital technology-based assessment model,” Eurasian Journal of Educational Research, vol. 2022, no. 98, pp. 203–220, 2022, doi: 10.14689/ejer.2022.98.013.
B. Gao, “Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities,” Sci Program, vol. 2022, 2022, doi: 10.1155/2022/4667677.
S. L. Happy, A. Dasgupta, P. Patnaik, and A. Routray, “Automated Alertness and Emotion Detection for Empathic Feedback During E-Learning.”
M. Chen, X. Liang, and Y. Xu, “Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/5993839.
A. Sassi, W. Jaafar, S. Cherif, J. Ben Abderrazak, and H. Yanikomeroglu, “Video Traffic Analysis for Real-Time Emotion Recognition and Visualization in Online Learning,” IEEE Access, vol. 11, pp. 99376–99386, 2023, doi: 10.1109/ACCESS.2023.3313973.
D. Yang, A. Alsadoon, P. W. C. Prasad, A. K. Singh, and A. Elchouemi, “An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment,” Procedia Comput Sci, vol. 125, no. 2009, pp. 2–10, 2018, doi: 10.1016/j.procs.2017.12.003.
D. Y. Liliana, T. Basaruddin, and I. I. D. Oriza, “The Indonesian Mixed Emotion Dataset (IMED): A Facial Expression Dataset for Mixed Emotion Recognition,” in Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality, in AIVR 2018. New York, NY, USA: Association for Computing Machinery, 2018, pp. 56–60. doi: 10.1145/3293663.3293671.
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