Article Per Year (5 Year)
p-Index From 2020 - 2025
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This Author published in this journals
All Journal Journal of Blockchain, Nfts and Metaverse Technology
Fajhar Muhammad
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm Fajhar Muhammad; Agung Triayudi; Eri Mardiani
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 2 No. 1 (2024): February 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v2i1.270

Abstract

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyses the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed.
Implementation of Face Recognition for Lecturer Attendance Using Deep Learning CNN Algorithm Fajhar Muhammad; Agung Triayudi; Eri Mardiani
SaNa: Journal of Blockchain, NFTs and Metaverse Technology Vol. 2 No. 2 (2024): August 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/sana.v2i2.275

Abstract

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyzes the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed
Co-Authors Agung Triayudi Eri Mardiani