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All Journal JIKO (Jurnal Informatika dan Komputer) International Journal of Informatics and Computation Jurnal Pengembangan Sistem Informasi dan Informatika Journal of Dinda : Data Science, Information Technology, and Data Analytics J-Intech (Journal of Information and Technology) Kharisma Tech Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal Of Artificial Intelligence And Software Engineering Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Journal of Information Technology Dharma Nusantara: Jurnal Ilmiah Pemberdayaan dan Pengabdian kepada Masyarakat
Bradika Almandin Wisesa
Politeknik Manufaktur Negeri Bangka Belitung
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : Journal of Information Technology

 1 
Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Silvia Agustin
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1927

Abstract

Face-based attendance systems are increasingly popular for their ease of use, but they are susceptible to fraud, such as using photos or videos for unauthorized attendance. This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. The dataset includes 30 facial images, encompassing both authentic and fraudulent samples. Testing demonstrates a facial recognition accuracy of 91.3% and effective spoofing detection in static and dynamic settings. This system provides a secure, fraud-resistant attendance solution ideal for educational and corporate settings. Further enhancements are suggested to improve performance across diverse facial expressions and lighting conditions.
Drowsiness Detection using YOLOv12 Wisesa, Bradika Almandin Almandin; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2212

Abstract

Drowsiness poses significant risks in safety-critical activities such as driving, industrial operations, and online learning. While advanced deep learning models (e.g., CNN-LSTM hybrids) achieve high accuracy in driver drowsiness detection, they often require substantial computational resources, limiting deployment on embedded or resource-constrained devices. This study addresses the research gap in lightweight, real-time, non-invasive drowsiness detection by developing an embeddable library using YOLOv12, an attention-centric single-stage detector known for balancing speed and accuracy. The model was trained on a custom dataset of 2312 video frame sequences (1011 "awake" and 1301 "drowsy" states, captured from varied angles under consistent lighting), augmented with standard techniques (e.g., brightness/contrast adjustments, flips, and rotations) to enhance generalization. It was evaluated through 80 real-time trials across multiple subjects. Performance metrics include accuracy of 93%, precision of 0.94, recall of 0.91, and F1-score of 0.93. The system detects drowsiness via facial bounding boxes followed by state classification (integrating eye/mouth aspect ratios) in real time. The main contribution is a proof-of-concept YOLOv12-based approach for non-invasive drowsiness monitoring, offering faster inference suitable for embedded applications (e.g., vehicle systems, meeting tools, or industrial safety) compared to heavier hybrid models. Limitations include some remaining sensitivity to extreme lighting/angles and dataset scale; future work will expand datasets, incorporate multi-modal cues, and further test robustness in diverse real-world conditions.
Co-Authors Andriyani, Widyastuti Better Swengky Dany Candra Febrianto Darma, Satria Agus Edyyul, Ilham Akerda  Evvin Faristasari Evvin Faristasari Evvin Faristasari Fadila, Resma Hengki Hengki Indra Irawan Linda Fujiyanti LINDA FUJIYANTI Lionza, Rahmat Lisda Lisda M. Hizbul Wathan Muhammad Raihan Pasha Nenen Isnaeni Pasha, Muhammad Raihan Peprizal, Peprizal Putri Armilia Prayesy Satria Agus Darma Silvia Agustin Silvia Agustin Silvia Agustin Sirlus Andreanto Jasman Duli Sirlus Andreanto Jasman Duli Vivin Mahat Putri Vivin Mahat Putri Yuli Astuti Zain, M Syafrizal