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Classification of Damaged Road Images Using the Convolutional Neural Network Method
Abstract
Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.
Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.
Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.
Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images.Full Text:
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F. N. Cahya, N. Hardi, D. Riana, and S. Hadiyanti, “Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network (CNN),” Sistemasi, vol. 10, no. 3, p. 618, 2021, doi: 10.32520/stmsi.v10i3.1248.
I. W. Suartika E. P, “Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, p. 76, 2016, [Online]. Available: http://repository.its.ac.id/48842/.
R. D. Kusumanto, A. N. Tompunu, and S. Pambudi, “Klasifikasi Warna Menggunakan Pengolahan Model Warna HSV Abstrak,” J. Ilm. Tek. Elektro, vol. 2, no. 2, pp. 83–87, 2011.
B. S. Kurniawan, S. R. Sentinuwo, and O. A. Lantang, “Aplikasi Pengenal Citra Nomor Kendaraan Bermotor Mengunakan Metode Template Matching,” J. Tek. Inform., vol. 8, no. 1, 2016, doi: 10.35793/jti.8.1.2016.12199.
P. Citra, K. Inframerah, and T. Kit, “MENDETEKSI AREA INFLAMASI PADA TUBUH MANUSIA THE UTILIZATION OF THERMAL IMAGING INFRARED CAMERA TO DETECT INFLAMMA ....,” no. December, 2021, doi: 10.22146/teknosains.39672.
C. M. Maheshan and H. Prasanna Kumar, “Performance of image pre-processing filters for noise removal in transformer oil images at different temperatures,” SN Appl. Sci., vol. 2, no. 1, pp. 1–7, 2020, doi: 10.1007/s42452-019-1800-x.
P. H. Wijaya, R. Wulanningrum, and R. Halilintar, “Perbaikan Citra Dengan Menggunakan Metode Gaussian Dan Mean Filter,” pp. 100–105, 2021.
A. Sembiring, “Perbandingan Algoritma Mean Filter, Median Filter dan Wiener Filter pada Aplikasi Restorasi Citra RGB Terdegradasi Impulse Noise Menggunakan The Peak Signal To Noise Ratio (PSNR),” 2017, doi: 10.31227/osf.io/rt6we.
L. Liang, S. Deng, L. Gueguen, M. Wei, X. Wu, and J. Qin, “Neurocomputing Convolutional neural network with median layers for denoising salt- and-pepper contaminations,” Neurocomputing, vol. 442, pp. 26–35, 2021, doi: 10.1016/j.neucom.2021.02.010.
N. Cnn, R. Magdalena, S. Saidah, N. Kumalasari, C. Pratiwi, and A. T. Putra, “Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural,” vol. 7, no. 3, pp. 335–339, 2021.
P. J. Hennessy, T. J. Esau, A. W. Schumann, Q. U. Zaman, K. W. Corscadden, and A. A. Farooque, “Evaluation of cameras and image distance for CNN-based weed detection in wild blueberry,” Smart Agric. Technol., vol. 2, no. October 2021, p. 100030, 2022, doi: 10.1016/j.atech.2021.100030.
M. Ahmad, A. Farisi, and W. Astuti, “Klasifikasi Multi-label pada Hadis Sahih Bukhari Terjemahan Bahasa Indonesia Menggunakan Convolutional Neural Networks,” vol. 8, no. 5, pp. 10594–10604, 2021.
T. A. Kurnia, J. Endrasmono, R. Y. Adhitya, S. Identifikasi, A. Pelindung, and D. Apd, “( APD ) MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ( CNN ) Abstrak,” pp. 24–31, 2018.
P. N. Zakiya, L. Novamizanti, S. Rizal, and U. Telkom, “KLASIFIKASI PATOLOGI MAKULA RETINA MELALUI CITRA OCT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ( CLASSIFICATION OF PATHOLOGY OF MACULA RETINA THROUGH OCT IMAGE USING,” vol. 8, no. 5, pp. 5072–5082, 2021.
H. N. Al Falah and K. K. Purnamasari, “Implementasi Convolutional Neural Network Pada Pengenalan Tulisan Tangan,” no. 112, 2019.
I. A. Sabilla, “Arsitektur Convolutional Neural Network (Cnn) Untuk Klasifikasi Jenis Dan Kesegaran Buah Pada Neraca Buah,” Tesis, no. 201510370311144, pp. 1–119, 2020, [Online]. Available: https://repository.its.ac.id/73567/1/05111850010020-Master_Thesis.pdf.
DOI: https://doi.org/10.31315/telematika.v19i2.6460
DOI (PDF): https://doi.org/10.31315/telematika.v19i2.6460.g4668
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Status Kunjungan Jurnal Telematika