Deteksi Kerusakan Jalan Berdasarkan Citra Digital Menggunakan Convolutional Neural Network (CNN)
DOI:
https://doi.org/10.35870/jimik.v6i1.1192Keywords:
Detection, Damaged Road, CNNAbstract
Highways are a connection between an area or region to another destination. The rapid construction of highways in big cities is not comparable to the improvement and rearrangement of damaged roads in several areas. Most of the damaged roads are caused by heavy vehicle traffic or heavy loads with quite frequent intensity, as well as natural disasters such as floods and earthquakes. This of course disrupts the traffic system, and is quite dangerous for drivers who often pass through areas where there are many damaged roads. With these obstacles, this study aims to build a system that can detect road damage through digital image capture using the convolutional neural network method. The results of this study obtained a road damage detection accuracy value reaching 80%.
Downloads
References
Angulo, A., Vega-Fernández, J. A., Aguilar-Lobo, L. M., Natraj, S., & Ochoa-Ruiz, G. (2019). Road damage detection acquisition system based on deep neural networks for physical asset management. In Advances in Soft Computing: 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Xalapa, Mexico, October 27–November 2, 2019, Proceedings 18 (pp. 3-14). Springer International Publishing.
Azmi, K., Defit, S., & Sumijan, S. (2023). Implementasi convolutional neural network (CNN) untuk klasifikasi batik tanah liat sumatera barat. Jurnal Unitek, 16(1), 28-40.
Bhatia, Y., Rai, R., Gupta, V., Aggarwal, N., & Akula, A. (2022). Convolutional neural networks based potholes detection using thermal imaging. Journal of King Saud University-Computer and Information Sciences, 34(3), 578-588.
Budi, R., Harianto, R. A., & Setyati, E. (2023). Segmentasi Citra Area Tumpukan Sampah Dengan Memanfaatkan Mask R-CNN. INSYST: Journal of Intelligent System and Computation, 5(1), 58-64. https://doi.org/10.52985/insyst.v5i1.305.
Dari, S. W., & Triloka, J. (2022, August). Kajian Algoritme Mask Region-Based Convolutional Neural Network (Mask R-CNN) dan You Look Only Once (YOLO) Untuk Deteksi Penyakit Kulit Akibat Infeksi Jamur. In Prosiding Seminar Nasional Darmajaya (Vol. 1, pp. 132-138).
Nugroho, P. A., Fenriana, I., & Arijanto, R. (2020). Implementasi deep learning menggunakan convolutional neural network (CNN) pada ekspresi manusia. Algor, 2(1), 12-20.
Qotrunnada, F. M., & Utomo, P. H. (2022, February). Metode Convolutional Neural Network untuk Klasifikasi Wajah Bermasker. In PRISMA, Prosiding Seminar Nasional Matematika (Vol. 5, pp. 799-807).
Rizal, F., Hasyim, F., Malik, K., & Yudistira, Y. (2021). Implementasi Algoritma Convolutional Neural Networks (CNN) Untuk Klasifikasi Batik. COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi, 2(2), 40-47. https://plu.mx/plum/a/?doi=10.33650/coreai.v2i2.3365.
Sasmito, B., Setiadji, B. H., & Isnanto, R. (2023). Deteksi Kerusakan Jalan Menggunakan Pengolahan Citra Deep Learning di Kota Semarang. TEKNIK, 44(1), 7-14. https://doi.org/10.14710/teknik.v44i1.51908.
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
Wijaya, A. T., Putra, O. V., & Umami, J. (2021). Deteksi Jalan Berlubang Pada Citra Berkabut Menggunakan Convolutional Neural Network Dan Dark Channel Prior. Prosiding Sains Nasional dan Teknologi, 1(1). http://dx.doi.org/10.36499/psnst.v1i1.5035.
Wona, M. M. A., Asyifa, S. A., Virgianti, R., Hamid, M. N., Handoko, I. M., Septiani, N. W. P., & Lestari, M. (2023). Klasifikasi Batik Indonesia Menggunakan Convolutional Neural Network (CNN). Jurnal Rekayasa Teknologi Informasi (JURTI), 7(2), 172-179.
Yulianto, Y., & Wibowo, A. (2023). Deteksi Keretakan Jalan Aspal Menggunakan Metode Convolutional Neural Network. Power Syst, 4(2), 581-594.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
