Development of Deep Learning Applications for Face Recognition in Online Media to Determine Student Presence

  • Cahya Rahmad, ST., M.Kom., Dr. Eng.
  • Arie Rachmad Syulistyo, S.Kom., M.Kom
  • Alan Rizky Wardana Politeknik Negeri Malang
Keywords: Convolutional Neural Network, Face recognition, Feature extraction

Abstract

Attendance is done manually during online lectures, namely by asking all students to activate the camera on their respective devices so that faces are visible and attendance is done. To make attendance easier, facial recognition is the right way to solve this problem. The initial stage to perform face recognition is face detection and extracting student facial features. This feature will be compared with the student face dataset so that the method used can identify the detected student. The method used in this research is Convolutional Neural Network (CNN) for feature extraction and face recognition. The results of this study by conducting attendance in 6 classes of students at the State Polytechnic of Malang majoring in Informatics Engineering and Information Management resulted in an average attendance time per class of 4.466 seconds and face recognition accuracy was 77.27%.

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How to Cite
[1]
Cahya Rahmad, ST., M.Kom., Dr. Eng., Arie Rachmad Syulistyo, S.Kom., M.Kom, and Alan Rizky Wardana, “Development of Deep Learning Applications for Face Recognition in Online Media to Determine Student Presence”, JIP, vol. 8, no. 3, pp. 8-14, Jun. 2022.