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STUDENT VIRTUAL CLASS ATTENDANCE BASED ON FACE RECOGNITION USING CNN MODEL

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 3 (2024)

DOI
10.22146/ijccs.95824
ISSN
2460-7258, 1978-1520
Published
2024-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Attendance records are an important tool that can be used to include and broadcast member participation in an activity, including the learning process. In online learning classrooms, the process of recording attendance becomes challenging to do manually, thus an automatic attendance recording system is needed. The authentication process is important in developing an existing recording system to guarantee the correctness of the recorded data. In this research, a face authentication system was built to create a system for recording online class attendance to help integrate participant activities and participation in online class learning. The face recognition approach uses a Convolutional Neural Network (CNN) model specifically designed to automate student attendance in virtual classes. Student image data is taken from virtual classroom sessions and used to train a CNN model. This model can recognize and verify student identity in various lighting conditions and head positions. This research consists of several stages, namely data collection, artificial neural networks, use of facial recognition, dataset application stage, and facial recognition in video frames. The experimental results showed that there were 11193 samples studied and of these 11193 samples the distribution was even, namely 6.7%. In addition, the model performance results show an accuracy of 76.28%.

Keywords

Collaboration network of the authors

7 authors · 13 co-authorship links · 7 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Indonesia University of Education
  • Indonesia Open University
  • Universitas Halu Oleo
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Agfianto Eko PutraUniversitas Gadjah Mada1
Ika Purwanti NingrumUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">STUDENT VIRTUAL CLASS ATTENDANCE BASED ON FACE RECOGNITION USING CNN MODEL</dc:title>
	<dc:creator>Nursantika, Dian</dc:creator>
	<dc:creator>Piantari, Erna</dc:creator>
	<dc:creator>Huseani, Dwi Fitria Al</dc:creator>
	<dc:creator>Husaeni, Dwi Novia Al</dc:creator>
	<dc:creator>Urwah, Mushfani Ainul</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Attendance system; Face recognition;CNN; virtual smart classroom</dc:subject>
	<dc:description xml:lang="en-US">Attendance records are an important tool that can be used to include and broadcast member participation in an activity, including the learning process. In online learning classrooms, the process of recording attendance becomes challenging to do manually, thus an automatic attendance recording system is needed. The authentication process is important in developing an existing recording system to guarantee the correctness of the recorded data. In this research, a face authentication system was built to create a system for recording online class attendance to help integrate participant activities and participation in online class learning. The face recognition approach uses a Convolutional Neural Network (CNN) model specifically designed to automate student attendance in virtual classes. Student image data is taken from virtual classroom sessions and used to train a CNN model. This model can recognize and verify student identity in various lighting conditions and head positions. This research consists of several stages, namely data collection, artificial neural networks, use of facial recognition, dataset application stage, and facial recognition in video frames. The experimental results showed that there were 11193 samples studied and of these 11193 samples the distribution was even, namely 6.7%. In addition, the model performance results show an accuracy of 76.28%.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-07-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/95824</dc:identifier>
	<dc:identifier>10.22146/ijccs.95824</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 3 (2024): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/95824/39738</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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