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Setiawan, Rama Bhagadhara
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Computer Science & IT

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Attendance System Face Recognition Using Convolutional Neural Network (CNN) Setiawan, Rama Bhagadhara; Lukman, Nur
CoreID Journal Vol. 1 No. 3 (2023): November 2023
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v1i3.16

Abstract

This article discusses the development of technology in various fields, with a focus on the implementation of digital technology and machine learning. Digitalization has influenced various aspects of life, including education and tourism. Machine learning, particularly convolutional neural networks (CNNs) and deep learning play an important role in these advancements, with applications extending from biology to healthcare. Face recognition technology, as part of biometrics, is highlighted in this article, used in various contexts such as security and enterprise management. This research implements CNN and Haar Cascade Classifier methods to build a face recognition system in the context of library attendance. With the tests conducted, the system achieved 95% accuracy, showing a good ability to detect faces in various conditions. In conclusion, the CNN algorithm can produce an effective face recognition system for use in library attendance systems, with reliable performance and high accuracy.
Co-Authors Nur Lukman