DOI: https://doi.org/10.21456/vol6iss1pp1-10

Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM)

*Neneng Neneng  -  STMIK Teknokrat
Kusworo Adi  -  Universitas Diponegoro
Rizal Isnanto  -  Universitas Diponegoro

Citation Format:
Abstract

Texture is one of the most important features for image analysis, which provides informations such as the composition of texture on the surface structure, changes of the intensity, or brightness. Gray level co-occurence matrix (GLCM) is a method that can be used for statistical texture analysis. GLCM has proven to be the most powerful texture descriptors used in image analysis. This study uses the four-way GLCM 0o, 45o, 90o, and 135o. Support vector machine (SVM) is a machine learning that can be used for image classification. SVM has a high generalization capability without any requirement of additional knowledge, even with the high dimension of the input space. The data used in this study are the image of goat meat, buffalo meat, horse meat, and beef with shooting distance 20 cm, 30 cm and 40 cm. The result of this study shows that the best recognition rate of 87.5% was taken at a distance of 20 cm with neighboring pixels distance d = 2 in the direction GLCM 135o.

Fulltext View|Download
Keywords: Citra; GLCM; Tekstur; SVM.

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL) Penerapan Algoritma Minimax Pada Game Macan-macanan Metode Forward Chaining Untuk Penentuan Kelayakan Bisnis Usaha Mikro More recent articles
Most cited articles
Implementasi Metode Promethee Dan Borda Dalam Sistem Pendukung Keputusan Pemilihan Lokasi Pembukaan Cabang Baru Bank Pengukuran Kesadaran Keamanan Informasi Dan Privasi Pada Pengguna Smartphone Android Di Indonesia Perancangan Model Data Flow Diagram Untuk Mengukur Kualitas Website Menggunakan Webqual 4.0 Sistem Informasi Geografis Berbasis Web untuk Pemetaan Sebaran Alumni Menggunakan Metode K-Means Metode Quality Function Deployment Dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Pemilihan Perusahaan Penyedia Jasa Internet More cited articles
  1. Basset, O., Buquet, B., Abouelkaram, S., Delachartre, P., dan Culioli, J., 2000. Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69, 437-445
  2. Bharati, M. H., Liu, J. J. and Mac Gregor, J. F., 2004. Image texture analysis: methods and comparisons. Chemometrics and Intelligence Laboratory Systems, 72, 57–71
  3. Burges, C.J., 1998. A Tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 1–43
  4. Haralick, R.M., 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67, 786–804
  5. Haralick, R.M., Shanmugam, K., dan Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621
  6. Kadir, A., dan Susanto, A., 2012. Teori dan aplikasi pengolahan citra, Andi: Yogyakarta
  7. Kadir, A. dan Susanto, A., 2013. Teori dan Aplikasi Pengolahan Citra, Andi: Yogyakarta
  8. Kudo, M. and Sklansky. J., 2000. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33, 25-41
  9. Krutz, G.W., Gibson, H.G., Cassens, D.L. and Zhang, M., 2000. Colour vision in forest and wood engineering. Landwards, 55, 2–9
  10. Nello, C., dan Taylor, J.S., 2000. An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press
  11. Patel, D., Davies, E.R. and Hannah, I, 1996. The use of convolution operators for detecting contaminants in food images. Pattern Recognition, 29 (6), 1019–1030
  12. Prasetyo, E., 2012. Data Mining Konsep dan Aplikasi Menggunkan Matlab, Andi: Yogyakarta
  13. Prasetyo, E., 2014. Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Andi Publisher: Yogyakarta
  14. Salat, R. dan Osowski, S., 2004. Accurate fault location in the power transmission line using support vector machine approach, IEEE Trans. Power Syst., 19, 879–886
  15. Seetha, M., Muralikrishna, I.V., Deekshatulu, B.L., Malleswari, B.L., Nagaratna, Hedge, P., 2008. Artificial neural network and other methods of image classification. Journal of Theoretical and Applied Information Technology, 1039–1053
  16. Schwartz. W, Pedrini. H, 2006. Textured image segmentation based on spatial dependence usinga Markov random field model, IEEE International Conference on Image Processing, Atlanta, GA, USA, pp. 2449–2452
  17. Sun, D-W., 2000. Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering, 44, 245–249
  18. Siqueira, F.R.D., Schwartz, W.R., Pedrini, H., 2013. Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing, 120, 336–345
  19. Zheng, C., Sun, D-W., dan Zheng, L., 2006. Recent applications of image texture for evaluation of food qualities—a review. Trends in Food Science & Technology, 17, 113–128

Last update:

  1. Sentiment Analysis of Presidential Candidate Debates from YouTube Videos

    Ulima Inas Shabrina, Riyanarto Sarno, Ratih Nur Esti Anggraini, Agus Tri Haryono, Abdullah Faqih Septiyanto. 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 2024. doi: 10.1109/AIMS61812.2024.10512640

  2. HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM

    Christina Purnama Yanti, I Gede Andika. Jurnal Teknologi dan Sistem Komputer, 8 (2), 2020. doi: 10.14710/jtsiskom.8.2.2020.94-99

  3. Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm

    Rohmat Indra Borman, Farli Rossi, Yessi Jusman, Ashrani Aizzuddin Abd. Rahni, Syahrizal Dwi Putra, Arief Herdiansah. 2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2021. doi: 10.1109/ICE3IS54102.2021.9649677

  4. Heart Disease Detection from PSAX Echocardiography View using Ultrasound Portable Based on Machine Learning Method

    Jovan Josafat Jaenputra, Riyanto Sigit, Tri Harsono, Abraham Ahmad Ali Firdaus. 2024 International Electronics Symposium (IES), 2024. doi: 10.1109/IES63037.2024.10665785

  5. Combination of extraction features based on texture and colour feature for beef and pork classification

    A M Priyatno, F M Putra, P Cholidhazia, L Ningsih. Journal of Physics: Conference Series, 1563 (1), 2020. doi: 10.1088/1742-6596/1563/1/012007

  6. Classification of Beef and Lamb Patterns Using Conducting Polymer Sensor Series and Kohonen Algorithm Method

    Benrad Edwin Simanjuntak, Marhaposan Situmorang, Syahrul Humaidi, Marzuki Sinambela. Science and Technology Applications, 126 , 2023. doi: 10.4028/p-oj34o8

  7. Identifying Pork Raw-Meat Based on Color and Texture Extraction Using Support Vector Machine

    Sriwanti Ayu Aisah, Anif Hanifa Setyaningrum, Luh Kesuma Wardhani, Rizal Bahaweres. 2020 8th International Conference on Cyber and IT Service Management (CITSM), 2020. doi: 10.1109/CITSM50537.2020.9268892

Last update: 2025-05-16 18:28:31

  1. Combination of extraction features based on texture and colour feature for beef and pork classification

    A M Priyatno, F M Putra, P Cholidhazia, L Ningsih. Journal of Physics: Conference Series, 1563 (1), 2020. doi: 10.1088/1742-6596/1563/1/012007

  2. Image processing for snake indentification based on bite using Local Binary Pattern and Support Vector Machine method

    Hernawati N.P.A.U.D.. Journal of Physics: Conference Series, 127 (1), 2019. doi: 10.1088/1742-6596/1192/1/012007