(Universitas Negeri Medan, Indonesia)(2) * Marta Aulia Simangunsong
(Universitas Negeri Medan, Indonesia)(3) Teguh Arif Mediansyah
(Universitas Negeri Medan, Indonesia)(4) Zulkaidah Harahap
(Universitas Negeri Medan, Indonesia)(5) Cristina Elseria Rahelta
(Universitas Negeri Medan, Indonesia)(6) Harvei Desmon Hutahean
(Universitas Negeri Medan, Indonesia)(7) Fahmy Syahputra
(Universitas Negeri Medan, Indonesia)(8) Elsa Sabrina
(Universitas Negeri Medan, Indonesia)*corresponding author
AbstractConventional attendance methods often lead to queues, time inefficiency, and potential violation of health protocols, necessitating a fast, non-contact, and real-time attendance recording system. This research aims to design and implement a web-based attendance system as a local prototype using face recognition biometrics. The system was developed using Python with the Flask Framework and OpenCV. The core face recognition process combines Dlib's Pretrained CNN model for 128-dimensional feature vector extraction (face embedding) and the K-NN method for classification based on Euclidean Distance calculation. Testing results indicate that the system successfully performs accurate and real-time facial identification. The system is capable of automatically logging attendance times, providing audio feedback, and storing the attendance data recapitulation in an Excel (.xlsx) file. Thus, this system provides an effective and efficient non-contact attendance solution. KeywordsAttendance, Face Recognition, Dlib, CNN, K-NN, Flask, Localhost.
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DOIhttps://doi.org/10.57235/qistina.v4i2.7555 |
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