Penerapan Computer Vision untuk Deteksi Perilaku Pengendara sebagai Upaya Pencegahan Kecelakaan Lalu Lintas

Authors

  • David Alif Tiano Putra Universitas Djuanda
  • Elsa Aulia Universitas Djuanda
  • Ghaitsa Tsuraya Shofa Universitas Djuanda
  • Ibnu Ahmar Ihsanudin Universitas Djuanda
  • Indria Nurputri Pratiwi Universitas Djuanda
  • Intan Khalimatul Sa'diyah Universitas Djuanda
  • Jalal Universitas Djuanda

DOI:

https://doi.org/10.30997/karimahtauhid.v5i5.23838

Keywords:

Computer Vision, Pendeteksi Kantuk, Kecelakaan Lalu Lintas

Abstract

Kantuk pada pengendara merupakan penyebab dari 35% kecelakaan lalu lintas di Indonesia (Rahmat et al., 2023). Dalam upaya mengatasi masalah ini, diperlukan upaya untuk mencegah pengendara larut dalam kantuknya. Teknologi Computer Vision menjadi salah satu solusi opsi untuk membangun sistem yang dapat mengenali tanda kantuk atau kelelahan pada pengendara. Pendeteksian kantuk menjadi salah satu upaya yang masih dikembangkan hingga saat ini dan telah banyak membuahkan inovasi dalam pengembangannya. Pada jurnal ini, akan diuraikan berbagai macam inovasi teknologi Computer Vision dan ke-efektifannya untuk dijadikan opsi dalam pengembangan pendeteksi kantuk pada pengendara.             

References

Abe, T. (2023). PERCLOS-based technologies for detecting drowsiness: current evidence and future directions. In SLEEP Advances (Vol. 4, Issue 1). Oxford University Press. https://doi.org/10.1093/sleepadvances/zpad006

Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021). Cnn variants for computer vision: History, architecture, application, challenges and future scope. In Electronics (Switzerland) (Vol. 10, Issue 20). MDPI. https://doi.org/10.3390/electronics10202470

Chen, G., Hong, L., Dong, J., Liu, P., Conradt, J., & Knoll, A. (2020). EDDD: Event-Based Drowsiness Driving Detection through Facial Motion Analysis with Neuromorphic Vision Sensor. IEEE Sensors Journal, 20(11), 6170–6181. https://doi.org/10.1109/JSEN.2020.2973049

Chengula, T. J., Mwakalonge, J., Comert, G., Sulle, M., Siuhi, S., & Osei, E. (2024). Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection. Machine Learning with Applications, 17, 100580. https://doi.org/10.1016/j.mlwa.2024.100580

Choi, J., Choi, E., Choi, S., & Lee, W. (2025). E-BTS: A low-power Event-driven Blink Tracking System with hardware-software co-optimized design for real-time driver drowsiness detection. Alexandria Engineering Journal, 128, 867–877. https://doi.org/10.1016/j.aej.2025.07.020

Jon Gutiérrez-Zaballa, Koldo Basterreetxea, Javier Echanobe, M. Victoria, & Ines del Campo. (2024). Exploring fully convolutional networks for the segmentation of hyperspectral imaging applied to advanced driver assistance system* (K. Desnos & S. Pertuz, Eds.; Vol. 13425). Springer International Publishing. https://doi.org/10.1007/978-3-031-12748-9

Dewi, C., Chen, R. C., Jiang, X., & Yu, H. (2022). Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks. PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.943

Dhasarathan, C., Gnanasekaran, S., Pattanayak, A., Kumar, G., Vig, K., Narain, V., Deva Narayan, K. M., & Garg, S. (2025). Tensor RT optimized driver drowsiness detection system using edge device. Ain Shams Engineering Journal, 16(10). https://doi.org/10.1016/j.asej.2025.103620

Feng, B., Zhu, R., Zhu, Y., Jin, Y., & Ju, J. (2025). Dynamic Vision Sensor-Driven Spiking Neural Networks for Low-Power Event-Based Tracking and Recognition. Sensors, 25(19), 6048. https://doi.org/10.3390/s25196048

Firdaus, A., Utaminingrum, F., & Widasari, E. R. (2023). Sistem Pendeteksi Kantuk Pengemudi berbasis Eye Aspect Ratio dan Mouth Opening Ratio menggunakan Algoritme C-LSTM (Vol. 7, Issue 2). http://j-ptiik.ub.ac.id

Fu, B., Boutros, F., Lin, C.-T., & Damer, N. (2024). A Survey on Drowsiness Detection -- Modern Applications and Methods. http://arxiv.org/abs/2408.12990

Gambar, P., Lamudur, G., & Sihombing, A. (n.d.). Literature Review: Metode Jaringan Neural Konvolusi (CNN) Untuk.

Gutiérrez-Zaballa, J., Basterretxea, K., Echanobe, J., Martínez, M. V., Martinez-Corral, U., Mata-Carballeira, Ó., & del Campo, I. (2023). On-chip hyperspectral image segmentation with fully convolutional networks for scene understanding in autonomous driving. Journal of Systems Architecture, 139. https://doi.org/10.1016/j.sysarc.2023.102878

Habibah, M. U., Kurniawan, M., & Korespondensi, P. (n.d.). SEGMENTASI CITRA WAJAH DENGAN IMPLEMENTASI ADAPTIF THRESHOLD-INTEGRAL IMAGE FACE IMAGE SEGMENTATION WITH IMPLEMENTATION OF ADAPTIVE THRESHOLD-INTEGRAL IMAGE. https://doi.org/10.25126/jtiik.202183840

He, X., Zhao, D., Li, Y., Shen, G., Kong, Q., & Zeng, Y. (2024). An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain. https://github.com/Brain-Cog-Lab/Transfer-for-DVS.

Hutamaputra, W., & Utaminingrum, F. (2021). Implementasi Facial Landmark dalam Pengenalan Wajah pada Sistem Pembayaran Elektronik (Vol. 5, Issue 5). http://j-ptiik.ub.ac.id

Iddrisu, K., Shariff, W., & Little, S. (2024). A Framework for Pupil Tracking with Event Cameras. http://arxiv.org/abs/2407.16665

Kang, D., & Ma, L. (2021). Real-Time Eye Tracking for Bare and Sunglasses-Wearing Faces for Augmented Reality 3D Head-Up Displays. IEEE Access, 9, 125508–125522. https://doi.org/10.1109/ACCESS.2021.3110644

Kumar, P. (n.d.). Face Detection By Open CV. https://doi.org/10.13140/RG.2.2.16043.31521

Kuwahara, A., Nishikawa, K., Hirakawa, R., Kawano, H., & Nakatoh, Y. (2022). Eye fatigue estimation using blink detection based on Eye Aspect Ratio Mapping(EARM). Cognitive Robotics, 2, 50–59. https://doi.org/10.1016/j.cogr.2022.01.003

Liu, Q., Wu, Z., Jia, X., Xu, Y., & Wei, Z. (2021). From local to global: Class feature fused fully convolutional network for hyperspectral image classification. Remote Sensing, 13(24). https://doi.org/10.3390/rs13245043

Lv, H., Feng, Y., Zhang, Y., & Zhao, Y. (2021). Dynamic Vision Sensor Tracking Method Based on Event Correlation Index. Complexity, 2021. https://doi.org/10.1155/2021/8973482

Mulyana, D. I., & Edi. (2023). PENERAPAN FACE RECOGNITION DENGAN ALGORITMA VIOLA JONES DALAM SISTEM PRESENSI KEHADIRAN SISWA DAN GURU PADA SEKOLAH IDN BOARDING SCHOOL JONGGOL. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(3), 1749–1757. https://doi.org/10.35870/jimik.v4i3.398

Nasir, O., Aljaidi, M., Alsarhan, A., Alshammari, S. A., Albalawi, N. S., Alshammari, N. H., & Aldoghmi, A. Q. (2025). SAFE-DRIVE-AI: A CNN–LSTM–Attention Framework for Drowsiness Detection. Engineering, Technology & Applied Science Research, 15(5), 27594–27600. https://doi.org/10.48084/etasr.12725

Navastara, D. A., Putra, W. Y. M., & Fatichah, C. (2020). Drowsiness Detection Based on Facial Landmark and Uniform Local Binary Pattern. Journal of Physics: Conference Series, 1529(5). https://doi.org/10.1088/1742-6596/1529/5/052015

Nazari, S., & Amiri, M. (2025). An accurate and fast learning approach in the biologically spiking neural network. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-90113-0

Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K. V., Carion, N., Wu, C.-Y., Girshick, R., Dollár, P., & Feichtenhofer, C. (2024). SAM 2: Segment Anything in Images and Videos. http://arxiv.org/abs/2408.00714

Salman, R. M., Rashid, M., Roy, R., Ahsan, M. M., & Siddique, Z. (2021). Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD. http://arxiv.org/abs/2112.10298

Salvati, L., D’amore, M., Fiorentino, A., Pellegrino, A., Sena, P., & Villecco, F. (2021). On-road detection of driver fatigue and drowsiness during medium-distance journeys. Entropy, 23(2), 1–12. https://doi.org/10.3390/e23020135

Setiawan, R. A., Pradana, F., & Abdurrachman Bachtiar, F. (2021). Pengembangan Aplikasi Pendeteksi Kelelahan bagi Pengendara Mobil berbasis Android melalui Face Recognition (Vol. 5, Issue 11). http://j-ptiik.ub.ac.id

Shen, S., Zhao, D., Shen, G., & Zeng, Y. (2024). TIM: An Efficient Temporal Interaction Module for Spiking Transformer. http://arxiv.org/abs/2401.11687

Son, S. Bin, Park, S. H., & Lee, Y. K. (2024). A Comprehensive Study on Key Components of Grayscale-based Deepfake Detection. KSII Transactions on Internet and Information Systems, 18(8), 2230–2252. https://doi.org/10.3837/tiis.2024.08.010

Supiyandi Supiyandi, Andriani Sitorus, Nurul Fitriah, Havni Virul, & Syawaliah Putri Rangkuti. (2024). Pendeteksi Gerakan Pada Vidio Menggunakan Pyton dan OpenCV. Merkurius : Jurnal Riset Sistem Informasi Dan Teknik Informatika, 2(6), 334–343. https://doi.org/10.61132/merkurius.v2i6.522

Supiyandi Supiyandi, Tegar Ardiansyah, Sri Putri Balqis, Jundi Haqqoni, & Salsa Nabila Iskandar. (2024). Deteksi Wajah dalam Foto Menggunakan Teknologi Visi Komputer. Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer, 2(6), 32–39. https://doi.org/10.61132/mars.v2i6.490

Venkateswarlu, M., & Chirra, V. R. R. (2025). CNN-ViT: A multi-feature learning based approach for driver drowsiness detection. Array, 27. https://doi.org/10.1016/j.array.2025.100425

Vicente-Sola, A., Manna, D. L., Kirkland, P., Di Caterina, G., & Bihl, T. (2024). Spiking Neural Networks for event-based action recognition: A new task to understand their advantage. http://arxiv.org/abs/2209.14915

Vishnu, R., Vishvaragul, S., Srihari, P., Nithiavathy, R., & Asmitha Shree, R. (2021). Retraction: Driver drowsiness detection system with opencv and keras. In Journal of Physics: Conference Series (Vol. 1916, Issue 1). IOP Publishing Ltd. https://doi.org/10.1088/1742-6596/1916/1/012172

Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., Girshick, R., & Research, F. A. (n.d.). Early Convolutions Help Transformers See Better.

Yuen, S. C. K., Zakaria, N. H. B., Su, G. E., Hassan, R., Kasim, S., & Sutikno, T. (2024). Real-time smart driver sleepiness detection by eye aspect ratio using computer vision. Indonesian Journal of Electrical Engineering and Computer Science, 34(1), 677–686. https://doi.org/10.11591/ijeecs.v34.i1.pp677-686

Dewi, C., Chen, R. C., Chang, C. W., Wu, S. H., Jiang, X., & Yu, H. (2022). Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety. Electronics (Switzerland), 11(19). https://doi.org/10.3390/electronics11193183

Neumann, T. (2024). Analysis of Advanced Driver-Assistance Systems for Safe and Comfortable Driving of Motor Vehicles. Sensors, 24(19). https://doi.org/10.3390/s24196223

Downloads

Published

2026-05-08

How to Cite

Putra, D. A. T., Aulia, E., Shofa, G. T., Ihsanudin, I. A., Pratiwi, I. N., Sa’diyah, I. K., & Jalal. (2026). Penerapan Computer Vision untuk Deteksi Perilaku Pengendara sebagai Upaya Pencegahan Kecelakaan Lalu Lintas. Karimah Tauhid, 5(5), 2227–2241. https://doi.org/10.30997/karimahtauhid.v5i5.23838

Similar Articles

<< < 6 7 8 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.