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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Appropriate Technology for Community Services JIKI (Jurnal Ilmu Komputer dan Informatika) Journal Software, Hardware and Information Technology Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Ticom: Technology of Information and Communication Jurnal Kabar Masyarakat Jurnal Penelitian dan Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems Journal of Computer Science Contributions (Jucosco) Jurnal Ticom: Technology of Information and Communication Jurnal Pengabdian Masyarakat Tekno
Aryasanti, Agnes
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Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Attendance System with Face Recognition Using K-Nearest Neighbor, Particle Swarm Optimization, and Histogram of Oriented Gradients Mardi Hardjianto; Agnes Aryasanti; Putri Hayati
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 4 (2026): December 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i4.15237

Abstract

Purpose – This study evaluates a contactless employee-attendance prototype designed to recognize faces using a camera near an entrance and register attendance without requiring employees to stop at a conventional device. Design/methods/approach – The prototype combined Histogram of Oriented Gradients (HOG) for facial feature representation, K-Nearest Neighbor (K-NN) for recognition, and Particle Swarm Optimization (PSO) for K-NN parameter optimization. The training set contained 180 facial images from 20 employees (nine images per employee). Evaluation used one image each from 27 participants, including 20 enrolled employees and seven individuals outside the enrollment set. Findings – Of the 27 test images, 25 produced the expected outcome, corresponding to 92.6% accuracy (TP = 18, FP = 0, FN = 2, TN = 7). Two enrolled employees were classified as unknown, while all seven unregistered participants were rejected. Recognition may be sensitive to illumination, pose, camera angle and image sharpness; the present test did not isolate these effects. Research implications/limitations – The small, single-image-per-participant test limits generalization, and the study does not report a direct K-NN versus PSO-optimized K-NN comparison. The prototype also responds to faces shown in photographs, indicating a presentation-attack vulnerability and a need for separate liveness evaluation. Originality/value – The work investigates the integration of established HOG, K-NN and PSO methods into an entrance-based attendance workflow. Its contribution is a prototype implementation and a bounded account of recognition outcomes and practical limitations rather than evidence of superiority over alternative classifiers or deployment-ready security.
Co-Authors Ari Irawan Ari Saputro, Ari Arinda Hana Syafira Chairunnisa Ayu Widiyastuti Chiara Angriana Dimas Ariyanto Dwi Achadiani Dwi Achadiani Farros, Ilham Goenawan Brotosaputro Grace Gata, Grace Hana Syafira, Arinda Hardjianto, Mardi Hendri Irawan Ilham Farros Indra Indra Jan Everhard Riworuhi Johan Santos Joko Christian Chandra Kusdiarto, Djati Martini Martini MUFTI MUFTI, MUFTI Pradana, Rizky Pudoli , Ahmad Pudoli, Ahmad Putri Hayati Ratna Kusumawardani Ratna Ujiandari Renaldi Setiawan Riri Irawati Ririt Roeswidiah Rismawandi, Rismawandi Rizky Pradana Rizky Pradana, Rizky Roeswidiah, Ririt Samsinar Samsinar Santos , Johan Setiawan, Renaldi Subandi Subandi Subandi Subandi Wiharto, Yudi