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Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron
https://doi.org/10.22146/ijeis.18260 Muhammad Irfan(1*), Bakhtiar Alldino Ardi Sumbodo(2), Ika Candradewi(3)
(1)
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
The evolution of video sensors and hardware can be used for developing traffic monitoring system vision based. It can provide information about vehicle passing by utilizing the camera, so that monitoring can be done automatically. It is needed for the processing systems to provide some information regarding traffic conditions. One such approach is to utilize digital image processing.
This research consisted of two phases image processing, namely the process of detection and classification. The process of detection using Haar Cascade Classifier with the training data image form the vehicle and data test form the image state of toll road drawn at random. While, Multilayer Perceptron classification process uses by utilizing the result of the detection process. Vehicle classification is divided into three types, namely car, bus and truck. Then the classification parameters were evaluated by accuracy.
The test results vehicle detection indicate the value of accuracy is 92.67. Meanwhile, the classification process is done with phase trial and error to evaluate the parameters that have been determined. Results of the study show the classification system has an average value of the accuracy is 87.60%.
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Candradewi, I., 2015, Pemrosesan Video untuk Klasifikasi Kendaraan Berbasis Support Vector Machine, Tesis, Pasca Sarjana Ilmu Komputer, Universitas Gadjah Mada, Yogyakarta.
Wang, G., Xiao, D., dan Gu, J., 2008, Review on Vehicle Detection Based on Video for Traffic Surveillance, Proceedings of the IEEE International Conference on Automation and Logistics, Qingdao, China, 2961 –2966.
Syndhuwardhana, F., 2010, Perancangan Pengaturan Sistem Traffic Light dengan Webcam Dinamis, Skripsi, Universitas Katolik Soegijarpranata, Semarang.
Handayani, A.M., 2014, Sistem Penghitung Jumlah Kendaraan Ringan Roda Empat Pada Jalan Raya dengan Metode Haar Cascade Classifier dan Camshift, Tesis, Universitas Gadjah Mada, Yogyakarta.
Culjak, I., Abram, D., Pribanic, T., Dzapo, H. dan Cifrek, M., 2012, A brief introduction to OpenCV, MIPRO, 2012 Proceedings of the 35th International Convention, Croatia.
Nagataries, D., Hardiristanto, S. dan Purnomo, M.H., 2012, Deteksi Obyek pada Citra Digital Menggunakan Algoritma Genetika untuk Studi Kasus Sel Sabit, http://digilib.its.ac.id/ITS-paper-22021120001182/21993, diakses tanggal 23 Oktober 2016.
Latifaf, D.A., Bambang, H. dan Wibowo, T.A., 2011, Klasifikasi Jenis Mobil Menggunakan Metode Backpropagation dan Deteksi Tepi Canny, Skripsi, Universitas Telkom, Bandung.
Viola, P. dan Michael, J.J., 2004, Robust Real-Time Face Detection, Internasional journal Of Computer Vision, 57(2), 137-154.
Trefny, J. dan Matas, J., 2010, Extebded Set of Local Binary Patterns for rapid Object Detection, Computer Vision Winter Workshop Februari 3-5, Libor Špacek and Vojtech Fran.
Puspitaningrum, D., 2006, Pengantar Jaringan Syaraf Tiruan, Edisi 1, Yogyakarta, ANDI.
DOI: https://doi.org/10.22146/ijeis.18260
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