Grey Level Co-Occurrence Matrix (GLCM) & Hybrid Klasifikasi untuk Mendeteksi Kerusakan Jalan Aspal

Ika Maylani, Virginia Wahyu Ambarwati, Bismar Wasykuru, Alqaroni Alqaroni, Firnanda Tri Buana Kusuma Wati

Abstract


The importance of detecting damage to the asphalt road surface is to minimize the occurrence of accidents caused by uneven road surfaces. Image extraction can be used to detect road surface damage. GLCM is a statistical method in which statistical calculations use the distribution of gray degrees (histograms) by measuring the level of contrast, granularity, and roughness of an area from the neighboring pixels in the image. The classification process uses a hybrid classification, which combines the SVM method with kernel changes and KNN with changes in k=3, 4, 5, 7 and 9 tracts.

Keywords


Citra, GLCM, Hybrid Clasification, asphalt road surface

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References


A. Irawan, A. Pratomo, M. Risa, and Heldiansyah, “Perancangan Sistem Deteksi Kerusakan Aspal Jalan Melalui Video Menggunakan Fatst Fourier Transform,” Pros. SNRT (Seminar Nas. Ris. Ter., vol. 5662, pp. 111–119, 2016.

A. Irawan, A. Pratomo, M. Risa, and Heldiansyah, “Perancangan Sistem Deteksi Kerusakan Aspal Jalan Melalui Video Menggunakan Fatst Fourier Transform,” Pros. SNRT (Seminar Nas. Ris. Ter., vol. 5662, pp. 111–119, 2016.

D. R. Sulistyaningrum, B. Setiyono, J. N. Anita, and M. R. Muheimin, “Measurement of Crack Damage Dimensions on Asphalt Road Using Gabor Filter,” J. Phys. Conf. Ser., vol. 1752, no. 1, 2021, doi: 10.1088/1742-6596/1752/1/012086.

T. Rateke and A. von Wangenheim, “Road surface detection and differentiation considering surface damages,” Auton. Robots, vol. 45, no. 2, pp. 299–312, 2021, doi: 10.1007/s10514-020-09964-3.

P. B. Prakoso, U. S. Lestari, and Y. Sari, “DETEKSI KERETAKAN PERMUKAAN PERKERASAN LENTUR JALAN RAYA ( STUDI KASUS : TANAH LUNAK DI BANJARMASIN ) Detection of Cracks on Highway Flexible Pavement Surfaces ( Case Study : Soft Soils in Banjarmasin ),” DETEKSI KERETAKAN PERMUKAAN PERKERASAN LENTUR JALAN RAYA ( Stud. KASUS TANAH LUNAK DI BANJARMASIN ) Detect. Cracks Highw. Flex. Pavement Surfaces ( Case Study Soft Soils Banjarmasin ), vol. 4, no. April, pp. 247–251, 2019, [Online]. Available: http://snllb.ulm.ac.id/prosiding/index.php/snllb-lit/article/view/194/195

S. Marianingsih, F. Utaminingrum, and F. A. Bachtiar, “Road surface types classification using combination of K-nearest neighbor and Naïve Bayes based on GLCM,” Int. J. Adv. Soft Comput. its Appl., vol. 11, no. 2, pp. 15–27, 2019.

C. Malegori, L. Franzetti, R. Guidetti, E. Casiraghi, and R. Rossi, “GLCM, an image analysis technique for early detection of biofilm,” J. Food Eng., vol. 185, pp. 48–55, 2016, doi: 10.1016/j.jfoodeng.2016.04.001.

Y. Park and J. M. Guldmann, “Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?,” Ecol. Indic., vol. 109, no. October 2019, p. 105802, 2020, doi: 10.1016/j.ecolind.2019.105802.

Z. Abbas, M. U. Rehman, S. Najam, and S. M. Danish Rizvi, “An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion,” Proc. - 2019 Amity Int. Conf. Artif. Intell. AICAI 2019, pp. 317–320, 2019, doi: 10.1109/AICAI.2019.8701374.

L. A. Demidova, “Two‐stage hybrid data classifiers based on svm and knn algorithms,” Symmetry (Basel)., vol. 13, no. 4, 2021, doi: 10.3390/sym13040615.

O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes classification using K-NN based on GLCM and HSV color space,” Proc. - 2017 Int. Conf. Innov. Creat. Inf. Technol. Comput. Intell. IoT, ICITech 2017, vol. 2018-Janua, pp. 1–6, 2018, doi: 10.1109/INNOCIT.2017.8319133




DOI: https://doi.org/10.31284/p.snestik.2023.4219

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