Implementation of Automatic Attendance System Based on Face Recognition Using CNN Method in IPB University Vocational School Environment

Dini Nurul Azizah
Raisa Mutia Thahir
Luthfi Dika Chandra
Muhammad Naufal Ardhani
Endang Purnama Giri
Gema Parasti Mindara

International Journal of Multilingual Education and Applied Linguistics
Vol. 1 No. 4 (2024): November : International Journal of Multilingual Education and Applied Linguist

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.