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All Journal PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Media Journal of General Computer Science (MJGCS)
Saputri, Rhadis Steffani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Detecting the Number of Students Using YOLOv11 to Prevent Proxy Attendance at Universitas Dinamika Bangsa Saputri, Rhadis Steffani; Apriliani, Aulia; Mukminin, Amirul
Media Journal of General Computer Science Vol. 2 No. 1 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i1.38

Abstract

Attendance is an important criterion for passing courses at Universitas Dinamika Bangsa Jambi. According to the academic regulations of Universitas Dinamika Bangsa Jambi, the minimum attendance requirement for course completion is 75%. The attendance process at the university utilizes an academic information system (SIAKAD) where students log in using a username and password, then scan an attendance barcode or input a unique code. Students often engage in proxy attendance practices, where they are marked present in the system despite being absent in reality. This study discusses the prevention of proxy attendance by employing a human detection system based on YOLOv11, capable of counting the number of students present in the classroom at Universitas Dinamika Bangsa Jambi. The research method involves the design, implementation, and evaluation of the system. This study adopts a deep learning approach using supervised learning methods for model training. The model is trained on a labeled dataset from Roboflow and implemented using the YOLOv11 algorithm. Based on the research results, the authors conclude that the human detection system is effective in counting the number of students in the classroom. However, the system still requires further development to detect criteria or features that can distinguish the detected individuals' status, specifically between students and lecturers.
Deteksi Dini Microsleep Pada Pengemudi Kendaraan Roda Empat Melalui Citra Mata Menggunakan Algoritma YOLOv8 Saputri, Rhadis Steffani; Apriliani, Aulia; Amar, Rizky Syahrul; Astri, Lola Yorita
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2529

Abstract

Microsleep adalah episode tidur singkat dan tiba-tiba yang terjadi tanpa kesadaran, terutama saat mengemudi, dan diketahui sebagai salah satu penyebab utama kecelakaan lalu lintas. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dini untuk microsleep pada pengemudi kendaraan roda empat menggunakan algoritma YOLOv8. Sistem ini beroperasi dengan mendeteksi kondisi mata secara real-time melalui kamera dan mengeluarkan peringatan baik yang dapat didengar maupun dilihat ketika mata tetap tertutup selama lebih dari 3 detik. Dataset yang digunakan adalah SleepyDetect dari Roboflow Universe, yang terdiri dari dua kelas yang relevan: Mata Terbuka dan Mata Tertutup. Model dibor menggunakan YOLOv8n. Hasil evaluasi menunjukkan kinerja yang sangat baik, dengan presisi 97,2%, recall 96,7%, F1-score 0,97, dan mAP@0.5 sebesar 97,9%. Sistem ini berhasil diimplementasikan dan diuji dalam berbagai kondisi pencahayaan, menunjukkan respons deteksi yang cepat dan akurat. Penelitian ini menunjukkan bahwa kombinasi visi komputer dan YOLOv8 dapat diterapkan secara efektif untuk meningkatkan keselamatan pengemudi melalui deteksi mikrotidur dini.
Co-Authors Amar, Rizky Syahrul Amirul Mukminin APRILIANI, AULIA Lola Yorita Astri, Lola Yorita