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

DOI: https://doi.org/10.21456/vol6iss1pp1-10
Article Info
Submitted: 10-03-2016
Published: 30-11-2016
Section: Research Articles

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.

Keywords

Citra; GLCM; Tekstur; SVM.

  1. Neneng Neneng 
    STMIK Teknokrat
  2. Kusworo Adi 
    Universitas Diponegoro
    Fakultas Sains dan Matematika
  3. Rizal Isnanto 
    Universitas Diponegoro
    Fakultas Teknik

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