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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics
Rasyid, Muhammad Akida Jabbar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Development of a Driver Drowsiness Detection System Using YOLO, EAR, and MAR for Driving Performance Evaluation Mahfudi, Isa; Rasyid, Muhammad Akida Jabbar; Adzikirani, Adzikirani; Kusumawardani, Mila; Soelistianto, Farida Arinie; Wijayanti, Rieke Adriati; Novianti, Atik
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.878

Abstract

Driver fatigue and drowsiness are among the major factors causing traffic accidents, leading to decreased alertness and slower reaction time. This study aims to develop a computer vision-based driver drowsiness detection system by integrating the You Only Look Once (YOLO), Eye Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR) methods, as well as to evaluate driving performance quantitatively. The system is designed to operate in real time using a camera as the visual data source, which is processed through face detection, facial landmark extraction, and the calculation of EAR and MAR parameters as indicators of eye condition and yawning activity. The experimental results show that the system is capable of classifying driver conditions into three categories: normal (alert), drowsy, and microsleep, with an average F1-score of 0.92. In addition, this study proposes the Alertness Index (AI) as a composite indicator calculated based on drowsiness frequency, eye closure duration, and yawning intensity to represent the driver’s alertness level more comprehensively. The system is also able to operate in real time with stable performance, supporting implementation in real-world conditions. The results demonstrate that the integration of YOLO, EAR, and MAR not only improves detection accuracy but also enables objective and data-driven driving performance evaluation. This system can be applied as a driver monitoring solution to improve road safety.
Co-Authors Adzikirani, Adzikirani ATIK NOVIANTI Isa Mahfudi Kusumawardani, Mila Rieke Adriati Wijayanti Soelistianto, Farida Arinie