Analisis Deteksi Mata Kantuk Di Wajah Pengemudi Menggunakan Support Vector Machine (SVM) Berbasis Citra Real-Time
- Ullya Dwi Maharani Politeknik Negeri Sriwijaya, Indonesia
-
Ade Silvia Handayani
*
Politeknik Negeri Sriwijaya,
Indonesia
- Lindawati Lindawati Politeknik Negeri Sriwijaya, Indonesia (*) Corresponding Author
Abstract
Traffic accidents in Indonesia are a serious issue with a high number of fatalities, and one of the main causes is microsleep, which is a brief moment of sleep while driving. To address this problem, this research has developed a sleePIness detection system based on the Internet of Things (IoT) using a Raspberry PI and a webcam, utilizing the Support Vector Machine (SVM) algorithm. The system is designed to detect the driver’s eye condition and provide a warning through a buzzer if the eyes are closed for more than 3 seconds. The research results indicate that the SVM model with a polynomial kernel has a training accuracy of 85.04%, demonstrating its ability to classify eye data into "opened" and "closed" categories. Evaluation with various SVM kernels, including linear, radial basis function (RBF), and polynomial, shows that the polynomial kernel performs the best with an accuracy of 85%, precision of 86%, and recall of 85% in detecting closed eyes. Although the system is effective in real-time detection of driver sleePIness, challenges remain with lighting conditions and camera positioning. Further testing is needed to improve the reliability and accuracy of the system in various situations. By providing early warnings to drivers, this system has significant potential to enhance road safety and prevent accidents caused by drowsiness.
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References
C. Aj. Saputra, D. Erwanto, and P. N. Rahayu, “Deteksi Kantuk Pengendara Roda Empat Menggunakan Haar Cascade Classifier Dan Convolutional Neural Network,” JEECOM Journal of Electrical Engineering and Computer, vol. 3, no. 1, 2021, doi: 10.33650/jeecom.v3i1.1510.
Y. Y. N. Adlina and S. Nurlaela, “Analisis Faktor Kecelakaan Lalu Lintas Surabaya Berdasarkan Perspektif Tata Ruang Melalui Pemodelan Spasial,” Jurnal Teknik ITS, vol. 10, no. 1, 2021, doi: 10.12962/j23373539.v10i1.59782.
“PMK Bikers Sambut Gerakan Indonesia Tertib dengan Touring Bagi Sembako | Kementerian Koordinator Bidang Pembangunan Manusia dan Kebudayaan.” Accessed: Jul. 25, 2024. [Online]. Available: https://www.kemenkopmk.go.id/pmk-bikers-sambut-gerakan-indonesia-tertib-dengan-touring-bagi-sembako
R. Rahmadiyani and A. widyanti, “Prevalence of drowsy driving and modeling its intention: An Indonesian case study,” Transp Res Interdiscip Perspect, vol. 19, 2023, doi: 10.1016/j.trip.2023.100824.
“Rancang Bangun Alat Pendeteksi Kantuk Pada Mobil Berbasis IoT Menggunakan Raspberry Pi Dan Kamera,” Jurnal Ilmiah Komputasi, vol. 20, no. 3, 2021, doi: 10.32409/jikstik.20.3.2797.
A. Hertig-Godeschalk, J. Skorucak, A. Malafeev, P. Achermann, J. Mathis, and D. R. Schreier, “Microsleep episodes in the borderland between wakefulness and sleep,” Sleep, vol. 43, no. 1, 2020, doi: 10.1093/sleep/zsz163.
J. Skorucak, A. Hertig-Godeschalk, D. R. Schreier, A. Malafeev, J. Mathis, and P. Achermann, “Automatic detection of microsleep episodes with feature-based machine learning,” Sleep, vol. 43, no. 1, 2020, doi: 10.1093/sleep/zsz225.
N. Ramadhani, S. Aulia, E. Suhartono, and S. Hadiyoso, “Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM,” Jurnal Rekayasa Elektrika, vol. 17, no. 2, 2021, doi: 10.17529/jre.v17i2.19884.
T. Arakawa, “A review of heartbeat detection systems for automotive applications,” 2021. doi: 10.3390/s21186112.
A. Asvin Mahersatillah Suradi, S. Alam, M. Furqan Rasyid, I. Djafar, U. Dipa Makassar, and J. K. Perintis Kemerdekaan, “Sistem Deteksi Kantuk Pengemudi Mobil Berdasarkan Analisis Rasio Mata Menggunakan Computer Vision,” JUKI : Jurnal Komputer dan Informatika, vol. 2, 2023.
M. Awais et al., “A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video,” IEEE J Biomed Health Inform, vol. 25, no. 5, 2021, doi: 10.1109/JBHI.2021.3073632.
R. A. Putri and N. Rochmawati, “Penerapan Algoritma Support Vector Machine untuk Klasifikasi Motif Citra Batik Solo Berdasarkan Fitur Multi-Autoencoders,” Journal of Informatics and Computer Science (JINACS), vol. 1, no. 01, 2019, doi: 10.26740/jinacs.v1n01.p56-63.
J. Li, “IOT security analysis of BDT-SVM multi-classification algorithm,” International Journal of Computers and Applications, vol. 45, no. 2, 2023, doi: 10.1080/1206212X.2020.1734313.
P. Madona and A. L. Tobing, “Early Detection of Microsleep in Motorcycle Helmet Based on Pulse Sensor,” Journal of Electronics Technology Exploration, vol. 1, no. 2, 2023, doi: 10.52465/joetex.v1i2.228.
T. Taufiq, “Deteksi Rasa Kantuk pada Pengendara Kendaraan Bermotor Berbasis Pengolahan Citra Digital,” Jurnal Teknologi Terapan and Sains 4.0, vol. 2, no. 1, 2021, doi: 10.29103/tts.v2i1.3850.
I. Mujahidin and B. F. Hidayatulail, “2.4 GHz SQUARE RING PATCH WITH RING SLOT ANTENNA FOR SELF INJECTION LOCKED RADAR,” JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science), vol. 2, no. 2, 2019, doi: 10.26905/jeemecs.v2i2.3253.
F. Denta Sukma and R. Mukhaiyar, “Alat Pendeteksi Ekspresi Wajah pada Pengendara Berbasis Image Processing,” JTEIN: Jurnal Teknik Elektro Indonesia, vol. 3, no. 2, 2022.
C. E. Panjaitan, D. Hagayna, D. Prandi, and R. Wiranto, “Integration Face Recognition and Body Temperature,” JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, vol. 5, no. 1, 2021, doi: 10.31289/jite.v5i1.5315.
K. Dickson, “Pengertian Baterai dan Jenis-jenisnya,” teknikelektronika.com, 2020.
S. Soim, “Development of Convolutional Neural Network Models to Improve Facial Expression Recognition Accuracy,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 2, pp. 279–289, 2024, doi: 10.26555/jiteki.v10i2.28863.
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Copyright (c) 2024 Ullya Dwi Maharani, Ade Silvia Handayani, Lindawati Lindawati
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