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Mochammad Fadiil Thoriq
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Computer Science & IT

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Implementasi Sistem Deteksi Kantuk Secara Real-Time Bagi Pengemudi Menggunakan Metode Eye Aspect Ratio Mochammad Fadiil Thoriq; Muhammad Fathi Ramdhana; Desinta Nur Rahma; Najla Amelia Putri; Rafi Hilal Zahir; Gema Parasti Mindara; Endang Purnama Giri
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 4 (2024): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i4.4226

Abstract

Traffic accidents are one of the leading causes of death worldwide, where drowsiness while driving is a significant factor that reduces driver alertness. This study develops a real-time driver drowsiness detection system using the Eye Aspect Ratio (EAR) method to avoid this. EAR calculates the ratio of the upper and lower eyelid distances to detect signs of drowsiness based on changes in eye shape. This system utilizes the OpenCV and Dlib libraries to identify faces and measure EAR, with a threshold of 0.25 as a warning trigger. If the EAR value drops below the threshold in several consecutive frames, the system automatically activates an alarm to increase driver alertness. With the advantages of cost efficiency and ease of implementation without additional hardware, this system is suitable for various types of vehicles. The results show that this system is effective in providing early warnings, thus helping to reduce the risk of accidents due to drowsiness.
Co-Authors Desinta Nur Rahma Endang Purnama Giri Gema Parasti Mindara Muhammad Fathi Ramdhana Najla Amelia Putri Rafi Hilal Zahir