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All Journal Jurnal Mantik
Juni S Juni S
Sekolah Tinggi Teknologi Dumai, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Enhancing road safety: machine learning-based driver drowsiness detection Ari Sellyana; Juni S Juni S
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4468

Abstract

Cars are a means of land transportation commonly used by humans. With cars, human activities can become more efficient and save time, especially when traveling. When driving a car, drivers must have a high level of focus and must obey the rules and prioritize safety when driving. A traffic accident is an unexpected and unintentional event involving vehicles or road users. The negative impacts of traffic accidents such as material loss, illness and death can affect the level of public health, vehicle factors and environmental factors. Of the several factors that cause accidents above, accidents are caused by humans. Accidents that occur are greatly influenced by the condition of the vehicle driver. Driver fatigue shows that sleepy drivers are the cause of road accidents. This research will develop a machine learning model to detect drowsiness in car drivers. The model will detect the driver's eyelid image and yawn condition. The data used is driver images which are collected and then processed using machine learning. The results of the study provide an overview of the level of drowsiness of car drivers.
Co-Authors Sellyana, Ari