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Yedizha Afif Farhani
Fauziah Fauziah
Nurhayati Nurhayati

Abstract

One of the solutions for the smooth flow of vehicles in big cities is to build toll road infrastructure. The toll road is a freeway with a paid system that can only be passed by four or more wheeled vehicles. There is often a buildup of vehicles when making payment transactions because the vehicle classification system is still manual or a wrong class occurs when payment transactions. To improve service quality, the use of deep learning to determine the class of vehicles or to correct transaction results is deemed effective. CNN method will classify vehicles based on two-dimensional image data. Vehicle data collection is done at the Halim Toll Gate..

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How to Cite
Farhani, Y. A., Fauziah, F. and Nurhayati, N. (2020) “Deep Learning Implementation to Determine Vehicle Groups at Halim Toll Gate: Deep Learning Implementation to Determine Vehicle Groups at Halim Toll Gate”, Jurnal Mantik, 4(1), pp. 260-266. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/739 (Accessed: 28March2024).
References
[1] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet Classificationwith Deep Convolutional Neural Networks”, Advances in Neural InformationProcessing Systems, pp. 1097–1105, 2012.
[2] Jan Wira Gotama Putra,“Pengenalan Konsep Pembelajaran Mesin dan Deep Learning”. Tokyo:https://wiragotama.github.io/
[3] Rama Adistya and M. Aziz Muslim “Deteksi dan Klasifikasi Kendaraan menggunakan Algoritma Backpropagation dan Sobel”, Journal of Mechanical Engineering and Mechatronics, Vol. 1 No. 2, pp. 65-73, ISSN: 2527-6212.
[4] Alvin Lazaro, Joko Lianto Buliali, and Bilqis Amaliah, “Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV” Jurnal Teknik ITS Vol. 6, No. 2, Hal. A293- A299, (2017), 2337-3520 (2301-928X Print).
[5] Fitroh Amaluddin, M. Aziz Muslim, and Agus Naba, “Klasifikasi Kendaraan Menggunakan Gaussian Mixture Model (GMM) dan Fuzzy Cluster Means (FCM)”, Jurnal EECCIS Vol. 9, No. 1, Juni 2015, Hal. 19-24.
[6] Dongbin Zhao, Yaran Chen, and Le Lv, “Deep Reinforcement Learning with Visual Attentionfor Vehicle Classification”, IEEE Transactions On Autonomous Mental Development, VOL.XXX , NO.XXX, 2016,DOI 10.1109/TCDS.2016.2614675.
[7] Renjie Xie, Heikki Huttunen, Shuoxin Lin, Shuvra S. Bhattacharyya, and Jarmo Takala, “Resource-Constrained Implementation and Optimization of a Deep Neural Networkfor Vehicle Classification”, 24th European Signal Processing Conference (EUSIPCO), 2016, 978-0-9928-6265-7/16/$31.00 ©2016 IEEE.
[8] Yiren Zhou, Hossein Nejati, Thanh-Toan Do, Ngai-Man Cheung, and Lynette Cheah, “Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study”, IEEE International Conference on Digital Signal Processing (DSP), 276-280, Aug 2016, ISSN: 2165.3577, DOI: 10.1109/ICDSP.2016.7868561.
[9] I Wayan E. P. S., Arya Yudhi Wijaya, and Rully Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101”, Jurnal Teknik ITS,Vol. 5, No. 1, (2016) ISSN: 2337-3539.
[10] Royani Darma Nurfita, and Gunawan Ariyanto, “Implementasi Deep Learning Berbasis Tensorflow untuk Pengenalan Sidik Jari”, Jurnal Emitor, Vol.18, No. 01, ISSN 1411-8890.
[11] BPJT. “Kepmen PU No 370/KPTS/M/2007”. http://bpjt.pu.go.id/konten/golongan-kendaraan. Diakses April 2020.
[12] Muhammad Irfan, Bakhtiar Alldino Ardi Sumbodo, and Ika Candradewi, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron”, IJEIS, Vol.7, No.2, 2017, pp. 139-148, ISSN: 2088-3714.
[13] Bagus Pribadi, and Muchammad Naseer, “Sistem Klasifikasi Jenis Kendaraan Melalui Teknik Olah Citra Digital”, SETRUM, Volume 3, No. 2, 2014,ISSN : 2301-4652.
[14] Yen-Yi Wu, and Chun-Ming Tsai, “Pedestrian, Bike, Motorcycle, and Vehicle Classification via Deep Learning: Deep Belief Network and Small Training Set”, International Conference on Applied System Innovation (ICASI), 2016, eISBN: 978-1-4673-9888-6, DOI: 10.1109/ICASI.2016.7539822.
[15] Danang Setiaji, and Herintaka, “Ekstraksi Fitur Bangunan Secara Cepat pada Foto UAV Menggunakan Metode Deep Residual Neural Network Berbasis FCN”, ELIPSOIDA, Vol. 02, No. 01, Hal. 42-49, 2019, ISSN: 2621-9883.
[16] He, et al,“Deep residual learning for image recognition”,IEEE Conference on Computer Vision and Pattern Recognition, 2016.
Situmorang, E., & Rindari, F. (2019). Decision Support System For Selection Of The Best Doctors In Sari Mutiara Hospital Using Fuzzy Tsukamoto Method. Jurnal Teknik Informatika C.I.T, 11(2, Septemb), 45-50. Retrieved from http://medikom.iocspublisher.org/index.php/JTI/article/view/12