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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Journal of Mechanical and Electrical Technology Jurnal Masyarakat Madani Indonesia Jurnal Pengabdian Kepada Masyarakat Hipotenusa: Journal of Mathematical Society Taawun: Jurnal Pengabdian kepada Masyarakat
Gillang Al Azhar
Politeknik Negeri Malang
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Other

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Design of Emergency Alarm System for Drowsiness Detection Using YOLO Method Based on Raspberry Pi Dinda Ayu Permatasari; Gillang Al Azhar; Muhammad Rifqi Zharfan; Anindya Dwi Risdhayanti; Arief Rahman Hidayat; Denda Dewatama
Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 2 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.6.2.93201

Abstract

Drowsiness is one of the main factors causing traffic accidents that often lead to fatalities, as drowsy drivers lose concentration. Therefore, drowsiness detection in car drivers is very important to prevent accidents. In this research, an emergency alarm system for drowsiness detection using YOLO method based on Mini PC is designed. This drowsiness detection system uses a camera to take pictures of the driver's face and the YOLO algorithm to detect the eyes. If the driver's eyes are detected to be closed, the system will give a warning in the form of a buzzer, display on the LCD, and water spray to wake up the driver. Test results show that the system has an average accuracy rate of 88% under optimal lighting conditions and 66.8% under low lighting conditions. The system also records detection and response times for further analysis, demonstrating the ability to not only detect the driver's condition but also record the time of the event. The data results show that the system is capable of detecting “Awake”, “Drowsy”, and “Asleep” states with an accuracy rate of 80%, as well as providing effective warnings to the driver when it detects signs of drowsiness.
Co-Authors Adi Candra Kusuma Adi Candra Kusuma Agung Ajie Sugiarto Agus Pracoyo Almas Adibah Anindya Dwi Risdhayanti Arief Rahman Hidayat Bambang Priyadi Dewatama, Denda Edi Sulistio Budi Hari Tri Wibowo Herwandi Herwandi Imam Saukani Leonardo Kamajaya M Imbarothur Mowaviq M Rifai Moh Rojikin Moh. Sulhan Muhammad Rifqi Zharfan Odhitya Desta Triswidrananta PERMATASARI, DINDA AYU Setiawan, Budhy Sidik Nurcahyo Subiyantoro Subiyantoro Syarifatul Izza Syarifatul Izza Wahyu Tri Wahono Winarno, Totok