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All Journal Jurnal Sarjana Teknik Informatika JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ekobistek Jurnal Sistim Informasi dan Teknologi Jurnal UNITEK Journal of Computer Scine and Information Technology Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi SATIN - Sains dan Teknologi Informasi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Emil Naf'an
Universitas Putra Indonesia YPTK Padang
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Edge AI-Based Multimodal Biometric Smart Reader Using YOLOv8 for Integrated Academic Attendance Systems Nurhadi Nurhadi; Emil Naf'an; Desyanti Desyanti; Mustazzihim Suhaidi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30527

Abstract

Attendance systems in vocational education institutions face challenges related to accuracy, security, and susceptibility to manipulation due to the use of single-modality authentication methods. RFID-based systems are vulnerable to card sharing, fingerprint systems suffer from latency during peak usage, and face recognition systems are sensitive to illumination and pose variations. This study proposes an Edge AI-based multimodal biometric smart reader integrating RFID, fingerprint, and YOLOv8-based face recognition for an academic attendance system at SMK Negeri 1 Dumai. The system is implemented on NVIDIA Jetson Nano as an edge computing device and integrated with an academic information system through an IoT-based architecture for real-time attendance monitoring. A decision-level fusion approach using majority voting is applied, where authentication is accepted if at least two of three modalities match. The system is evaluated using accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and response time. Experimental results show that the proposed multimodal system achieves an accuracy of 98.72%, outperforming RFID (89.34%), fingerprint (92.15%), and YOLOv8 face recognition (95.63%). The system also reduces FAR to 0.82% and FRR to 0.91%, with an average response time of 1.47 seconds, making it suitable for real-time deployment. Overall, the proposed Edge AI-based multimodal biometric system demonstrates high accuracy, improved security, and efficient real-time performance, providing a scalable solution for intelligent attendance systems in vocational education environments.
Co-Authors Ayu Fazzira Yuskar Desyanti Desyanti Eka Praja Wiyata Mandala Fajrul Islami Gushelmi Habib Akhyari Hadi Syahputra Ilham Saputra Laksamana, Ridho Leo Eka Putra M Nerdi Rafli M.Hafizh Mustazzihim Suhaidi Nanda Tommy W Nilda Tri Putri Novita Triana Nurhadi Nurhadi Nurhadi, Nurhadi Okta Andrica Putra Rahman, Sepsa Nur Ramdandi Rapi Ananda Retno Devita Selvi Zola Fenia Sylvia Kurniati Yogi Wiyandra