IMPLEMENTATION OF DEEP LEARNING ALGORITHM FOR PT GROWTH SUMATERA'S FACE DETECTION ATTENDANCE SYSTEM
DOI:
10.54443/ijset.v4i12.1552Published:
2026-01-19Downloads
Abstract
Attendance is a data collection activity to determine the number of employees present, arrival times, and departure times in a company. Attendance is divided into two types: manual and automatic. Manual attendance is an attendance process carried out using a handwritten note or signature form. Automatic attendance is an attendance process that involves technology. With facial recognition technology, an attendance system can be developed. Facial recognition technology is a computer technology that functions to determine facial location, facial size, feature detection, background image ignoring, and facial image identification. Facial recognition involves several variables, such as source images, processed images, extracted images, and a person's identity data. Deep learning with Convolutional Neural Networks is one method used to predict and classify different human facial images. This facial detection attendance system application is designed and built on a desktop platform, using the Python programming language. The application of deep learning algorithms with convolutional neural networks (CNN) in this facial detection attendance system can streamline the existing attendance system.
Keywords:
Face Detection Deep Learning Convolutional neural network CNNReferences
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Copyright (c) 2026 Norita Tampubolon, Penggabean Siahaan, Lewika Tampubolon, Zailani Sinabariba, Muhammad Syahputra Novelan
This work is licensed under a Creative Commons Attribution 4.0 International License.
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