The Application of Emotion Detection System (EmoD) in Online Learning

Authors

  • Suliana Sulaiman Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
  • Rohaizah Abdul Wahid Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
  • Marzita Mansor Department of Computer Science and Digital Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
  • Asma Hanee Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
  • Nor Hasbiah Ubaidullah Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia
  • Karnadi Teknologi Informasi, Fakultas Teknik Universitas Muhammadiyah Palembang, Palembang City, South Sumatra 30116, Indonesia

DOI:

https://doi.org/10.37934/araset.58.2.196210

Keywords:

Emotion recognition, ResNet 50, Online learning

Abstract

Measuring the student's emotions can help the teacher increase student engagement during Teaching and Learning (TnL) and simultaneously help the teachers measure the effectiveness of the TnL activity. However, most online TnL activities do not consider the learner's emotions and affection. Not all facial recognition methods can detect emotion, especially in real-time. To overcome this problem, we developed an Emotion Detection System (EmoD) to recognize and identify the emotion based on its class using ResNet 50. Using the System Usability Scale (SUS), the EmoD was reported as acceptable (mean = 78.5). Other than that, the EmoD system can be utilized for online consultation and classes. In the future, the EmoD will be enhanced so that it can be used to detect more than one user to make it more useful for online learning.

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Author Biographies

Suliana Sulaiman, Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia

suliana@meta.upsi.edu.my

Rohaizah Abdul Wahid, Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia

rohaizah@meta.upsi.edu.my

Marzita Mansor, Department of Computer Science and Digital Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia

marzita@meta.upsi.edu.my

Asma Hanee, Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia

asma@meta.upsi.edu.my

Nor Hasbiah Ubaidullah, Department of Software Engineering and Smart Technology, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak, Malaysia

hasbiah@meta.upsi.edu.my

Karnadi, Teknologi Informasi, Fakultas Teknik Universitas Muhammadiyah Palembang, Palembang City, South Sumatra 30116, Indonesia

karnadi@um-palembang.ac.id

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Published

2024-10-07

Issue

Online First Articles

Section

Articles
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