Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal IJEE (Indonesian Journal of English Education) Communications in Science and Technology Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) Prosiding Seminar Nasional dan Call For Paper Kebidanan Universitas Ngudi Waluyo Humaniorum Journal
Yenny Rahmawati
Syarif Hidayatullah State Islamic University Of Jakarta
Agriculture, Biological Sciences & Forestry Humanities Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Communications in Science and Technology

 1 
Evaluating the effectiveness of facial actions features for the early detection of driver drowsiness in driving safety monitoring system Rahmawati, Yenny; Woraratpanya, Kuntpong; Ardiyanto, Igi; Adi Nugroho, Hanung
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1594

Abstract

Traffic accidents caused by drowsiness continue to pose a serious threat to road safety. Many of these accidents can be prevented by alerting drivers when they begin to feel sleepy. This research introduces a non-invasive system for detecting driver drowsiness based on visual features extracted from videos captured by a dashboard-mounted camera. The proposed system utilizes facial landmark points and a facial mesh detector to identify key areas where the mouth aspect ratio, eye aspect ratio, and head pose are analyzed. These features are then fed into three different classification models: 1D-CNN, LSTM, and BiLSTM. The system’s performance was evaluated by comparing the use of these features as indicators of driver drowsiness. The results show that combining all three facial features is more effective in detecting drowsiness than using one or two features alone. The detection accuracy reached 0.99 across all tested models.
Co-Authors Alfurcaan, Reynaldi Andri Sutrisno Dewi Sagitaria, Bian Ekawati, Rahmi Ertin Ertin Haidi, Nasrul Hanung Adi Nugroho Igi Ardiyanto Kartika, Tantri Muhamad Imam Nurhayati Nurhayati Pambudi , S Untung Simanungkalit, Dohma P Nathanael Sri Murtiningsih, Sri Trianingsih Untari, Kurnia Sri Woraratpanya, Kuntpong Yulia Nur Khayati