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All Journal International Journal Software Engineering and Computer Science (IJSECS)
Ramadhan, Muhammad Adri
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Computer Science & IT

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Implementation of Haar Cascade and K-Nearest Neighbors (KNN) Face Recognition for Optimizing Warehouse Access Control Security Mulyana, Dadang Iskandar; Ramadhan, Muhammad Adri
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5244

Abstract

Warehouse facility access control security represents a critical factor in maintaining operational integrity and preventing criminal activities. This research addresses the elevated security threat risks associated with physical surveillance systems that continue to rely on manual methods with suboptimal performance. The study develops an automated security system based on face recognition technology, implementing Haar Cascade and K-Nearest Neighbors Classifier methods to identify and verify warehouse user identities with precision and automation. The research object focuses on facial recognition systems for warehouse access control. The methodology applies Haar Cascade algorithms for facial detection and K-Nearest Neighbors Classifier for classifying detected faces against existing datasets. Implementation utilizes external webcams, computer hardware, and Python-based programming software. Results demonstrate that the developed system achieves facial recognition accuracy exceeding 90%, delivering superior security performance compared to manual systems. The research concludes that face recognition technology effectively enhances efficiency and security in warehouse access management. The study recommends implementing such systems in large-scale warehouse facilities to optimize security management protocols
Co-Authors Dadang Iskandar Mulyana`