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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research Technomedia Journal Journal of Education Technology Aptisi Transactions on Management JTP - Jurnal Teknologi Pendidikan Aptisi Transactions on Technopreneurship (ATT) CCIT (Creative Communication and Innovative Technology) Journal ADI Journal on Recent Innovation (AJRI) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC)
Santoso, Nuke Puji Lestari
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Reliability Assessment of Attendance Systems Based on Face Recognition Under Varying Lighting Conditions Afiyanto, Rafid; Astuti, Eka Dian; Kamal, Abdullah Arif; Santoso, Nuke Puji Lestari
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.924

Abstract

The rapid adoption of face recognition technology for attendance systems has raised concerns about its reliability under varying lighting conditions, which often affect real world deployment. This study aims to analyze the reliability of a face recognition based attendance system under diverse lighting scenarios, addressing challenges in accuracy and robustness. The research employs a deep learning approach, utilizing a Convolutional Neural Network (CNN) trained on a dataset of facial images captured under controlled and uncontrolled lighting conditions, ranging from low to high illumination levels. The methodology includes preprocessing techniques for illumination normalization and feature extraction, followed by performance evaluation using metrics such as accuracy, precision, and false acceptance rate. Experimental results demonstrate that the proposed system achieves an accuracy of 92% in optimal lighting but drops to 78% under low light conditions, highlighting the impact of illumination on recognition performance. The integration of adaptive preprocessing techniques improves reliability by 12% in challenging scenarios. This study concludes that while face recognition based attendance systems are highly effective, their reliability in diverse lighting conditions can be significantly enhanced through advanced preprocessing and robust algorithm design, offering practical implications for real time biometric applications in dynamic educational and workplace settings.
Co-Authors Abbas, Maulana Afiyanto, Rafid Anggy Giri Prawiyogi Anwar, Aang Solahudin Apriliasari, Dwi Astriyani, Erna Astuti, Eka Dian Bennet, Daniel Bist, Ankur Singh Danang, Danang Danny Manongga Desy Apriani Dwi Andayani, Dwi Eko Sediyono Elmanda, Vonda Faturahman, Adam Fitra Putri Oganda Friandi, Sendy Zul Harahap, Eka Purnama Irwan Sembiring Juniar, Hega Lutfilah Juniar Kamal, Abdullah Arif Khoirunisa, Alfiah Kumoro, Dwi Ferdiyatmoko Cahya Marviola Hardini Maulana, Sabda Millah, Shofiyul Muhammad Iqbal Mulyati Mustofa Kamil, Mustofa Mustofa, Kenny Ilyas Natalia, Elisa Ananda Ninda Lutfiani Nur Azizah Nurmala, Risma Pangaribuan, Kelvin Po Abas Sunarya Qurotul Aini Ratri, Sanda Ramadhan Rawat, Bhupesh Rizki Galang Rahmadani Ruli Supriati, Ruli Salam, Rahmat Sandy Kosasi siti Nurindah Sari Siti Ria Zuliana, Siti Ria Sri Yulianto Joko Prasetyo Sulistiawati Sulistiawati Suryari Purnama Tangkaw, Melani Rapina Untung Rahardja Wahid, Syahrul Mu'Arif Xolani, Zanele