Analisis Klasifikasi Mobil Pada Gardu Tol Otomatis (GTO) Menggunakan Convolutional Neural Network (CNN)
The concept of a smart city is the most important issue in the development aspect of big cities in the world. Where the city must promise a more comfortable, organized, healthy and efficient life. Smart transportation is part of a smart city that is useful for improving better urban planning. Smart transportation also applies to toll roads, such as automating toll road retribution payments. Automatic Toll Gate (GTO) in Indonesia still uses sensors. However, sensors often misclassify trailers. In addition, the use of sensors also requires additional costs in installation and maintenance. Currently, every toll gate is equipped with cameras for various purposes. By utilizing the camera for vehicle type classification, the cost of the GTO will be reduced. For this reason, utilizing a digital camera with computer vision for vehicle type classification is the solution. Convolutional Neural Networks (CNN) is the most popular technique today in solving computer vision problems. Exploit the existing CNN by replacing the last fully connected output according to the number of vehicle classes. The test results show that mobilenet V2 is better in the classification of vehicle types, the best accuracy is Alexnet 93.81% and Mobilenet 96.19%. Computer vision by utilizing CNN is expected to replace the use of sensors so that implementation costs are cheaper.
I. BPS, “www.bps.go.id Pada tanggal 24 Mei 2021,” 2019. https://www.bps.go.id/indicator/17/57/1/jumlah-kendaraan-bermotor.html.
L. S. Hsb, P. Hariani, and J. S. Hsb, “City Smart Transportation Sebagai Strategi Medan Menuju Smart City,” J. Pembang. Perkota., vol. 5, no. 2, pp. 50–58, 2017.
W. G. Triatmojo, “Begini Cara Gerbang Tol Mendeteksi Jenis Dan Golongan Kendaraan,” widodogroho.com, 2019. https://www.widodogroho.com/2019/08/begini-cara-gerbang-tol-mendeteksi.html (accessed Oct. 25, 2021).
S. Rahman, M. Ramli, F. Arnia, A. Sembiring, and R. Muharar, “Convolutional Neural Network Customization for Parking Occupancy Detection,” in 2020 International Conference on Electrical Engineering and Informatics (ICELTICs), 2020, pp. 1–6.
W. Swastika, M. F. Ariyanto, H. Setiawan, and P. L. T. Irawan, “Appropriate CNN Architecture and Optimizer for Vehicle Type Classification System on the Toll Road,” in Journal of Physics: Conference Series, 2019, vol. 1196, no. 1, p. 12044.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510–4520.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
W. Fajariyah, “Penyelesaian wanprestasi pada perjanjian sewa-menyewa convolutional mobil di rental AR Malang tinjauan Kompilasi Hukum Ekonomi Syariah.” Universitas Islam Negeri Maulana Malik Ibrahim, 2014.
A. Damuri, R. Anagora, G. Hendratna, and A. S. Putra, “Konsep Kota Pintar Yang Diterapkan Pada Sistem Gardu Tol Otomatis (Gto),” IKRA-ITH Inform. J. Komput. dan Inform., vol. 4, no. 3, pp. 47–56, 2020.
W. Sugiarto, Y. Kristian, and E. R. Setyaningsih, “Estimasi Arah Tatapan Mata Menggunakan Ensemble Convolutional Neural Network,” Teknika, vol. 7, no. 2, pp. 94–101, 2018.
T. Nurhikmat, “Implementasi Deep Learning Untuk Image Classification Menggunakan Algoritma Convolutional Neural Network (CNN) Pada Citra Wayang Golek,” 2018.
G. Aurélien, “Hands-on machine learning with scikit-learn & tensorflow,” Geron Aurelien, 2017.
I. Putra, “Klasifikasi citra menggunakan convolutional neural network (CNN) pada caltech 101.” Institut Teknologi Sepuluh Nopember, 2016.
A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv, 2017.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep neural networks. Advances in neural information processing systems. 2012;25.
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