Comparison of Meat Type Classification Results Using Texture Feature Extraction of Gray Level Co-occurrence Matrices (GLCM) and Local Binary Pattern (LBP)


Neneng Neneng Ajeng Savitri Puspaningrum Ahmad Ari Aldino


Meat has high nutritional value which is widely consumed by humans. The content contained in meat includes protein, vitamins, minerals, fats, and other substances that are needed by the body so that it can carry out activities every. However, unfortunately not all people can distinguish these types of meat, because the texture and color are almost similar. This is also often used by irresponsible meat sellers by mixing these types of meat or with other types of meat to get a big profit. Even though not everyone can consume certain types of meat for reasons of illness. This research was conducted to compare the GLCM and LBP methods for image classification of meat types based on texture analysis. The types of meat images that are classified are goat meat, buffalo meat, and horse meat. Image data is taken manually using a digital camera the Nikon D3200. The image was taken at a distance of 20 cm. Data testing and training was carried out using the Support Vector Machine (SVM) method. The texture characteristics used are ASM, IDM, entropy, contrast, and correlation. The results of the image classification accuracy of goat, buffalo, and horse meat using the GLCM method were 75.6%. While the results of the classification accuracy using the LBP method amounted to 85,6%. Thus, the LBP texture feature extraction method is recommended for classification of meat types using texture characteristics.


[1] A. Suresh and K. L. Shunmuganathan, “Image texture classification using gray level co-occurrence matrix based statistical features,” Eur. J. Sci. Res., 2012.
[2] D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, “Local binary patterns and its application to facial image analysis: A survey,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews. 2011, doi: 10.1109/TSMCC.2011.2118750.
[3] N. Neneng, K. Adi, and R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM),” J. Sist. Inf. BISNIS, 2016, doi: 10.21456/vol6iss1pp1-10.
[4] Y. Fernando, “Klasifikasi Jenis Daging Berdasarkan Analisis Citra Tekstur Gray Level Co-Occurrence Matrices ( Glcm ) Dan Warna,” in Seminar Nasional Sains dan Teknologi 2017, 2017, no. Fakultas Teknik Universitas Muhammadiyah Jakarta, pp. 1–7.
[5] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: Application to face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., 2006, doi: 10.1109/TPAMI.2006.244.
[6] D. Kita, A. W. Widodo, and M. A. Rahman, “Ekstraksi Ciri pada Klasifikasi Tipe Kulit Wajah Menggunakan Metode Local Binary Pattern,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., 2019.
[7] S. Styawati and K. Mustofa, “A Support Vector Machine-Firefly Algorithm for Movie Opinion Data Classification,” IJCCS (Indonesian J. Comput. Cybern. Syst., 2019, doi: 10.22146/ijccs.41302.
[8] H. Sulistiani and A. A. Aldino, “Decision Tree C4.5 Algorithm For Tuition Aid Grant Program Classification (Case Study: Department Of Information System, Universitas Teknokrat Indonesia),” Edutic - Sci. J. Informatics Educ., 2020, doi: 10.21107/edutic.v7i1.8849.
[9] D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass Svm Pada Opini Publik Berbahasa Indonesia Di Twitter,” j. Teknokompak, 2020.