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All Journal Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Journal of Computer Networks, Architecture and High Performance Computing International Journal Software Engineering and Computer Science (IJSECS) Data Sciences Indonesia (DSI) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Yennimar Yennimar
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Physics Social Sciences

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Driver Drowsiness Detection Using Multi-Metric Modeling Based on Facial Landmarks Ferry Angga Wijaya; T. Tamil Arsen; Ayu Endang Syah Putri; Mika Damayanti; Yennimar Yennimar
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7106

Abstract

Drowsiness is a major factor contributing to traffic accidents, as it significantly reduces driver alertness, reaction time, and decision-making ability. This study aims to develop a real-time driver drowsiness detection system based on multi-metric modeling using facial landmarks. Three physiological indicators were employed: Eye Aspect Ratio (EAR) to measure eye openness, Mouth Aspect Ratio (MAR) to identify yawning activity, and Percentage of Eye Closure (PERCLOS) to assess prolonged eye closure patterns. These features were extracted using MediaPipe Face Landmarker, a lightweight and efficient facial landmark detection framework. A quantitative approach with a rule-based method was applied without requiring machine learning training, making the system computationally efficient and easily deployable. Sliding window smoothing was incorporated to reduce false detections and improve overall detection stability. The system was implemented as an Android mobile application and evaluated in real-time conditions using the device's front camera. Experimental results demonstrate that PERCLOS serves as the most stable and reliable drowsiness indicator, while the integration of all three metrics yields significantly more accurate detection compared to relying on a single indicator alone. This system offers a promising non-intrusive, accessible, and practical solution for real-time driver monitoring.
Co-Authors Amir Amir Anita Christine Sembiring Anita Yose Fanny Manurung Annisa Fadhlila Antonius Nyomadi Ayu Endang Syah Putri Ayu Widila Bayu Gunara Charles Charles Christin Erniati Panjaitan Cindy Cindy Denny Peter Leonardo Dewi S. Sihotang Dini M. Hutagalung Dono Sitinjak Estomih Wau Evan Chandra Sibarani Fachrul Rozi Ferry Angga Wijaya Gerson Tandiono Irwansyah Irwansyah Ivan Leonardo Silaban Jodi Alexsander Laia Johanes T. Gultom Julfan Farman Zebua kelvin kelvin Kristina Ginting Laila Hendrina Landong Hutasoit Lena LH Hasibuan M. Anhar Guci M. Rafi Faturrahman Mawaddah Harahap Mika Damayanti Miko Pasaribu Muhammad Iqbal Tri Rahmadi Muhammad Yusuf Pradana Yusna Pratiwi Pratiwi Ray Sanjaya Gulo Reinhard Oktaveri Gultom Reni Lusiana Simangunsong Reza Syahputra Risda Amelia Amanta Br Ginting Samuel Rifaldi Pasaribu Siregar, Saut Dohot Siwa Nesen Sri Wahyuni Tarigan Standley Malvin Japardy Stephen Boy Kristian Tafonao Steven Steven Sunarly Wijaya Suwandi Suwandi Syandi Nainggolan T. Tamil Arsen Toppan Sintio Uni Pratama Tarigan Widya Fernanda Putri Wikesyah Putri Sitorus William S. Purba Yans Y.M.P. Situmorang