<![CDATA[Due to high consumer demand, some traders use the high price of meat to make a profit by mixing pork and buffalo meat. Some consumers are not aware of this, because in plain view buffalo meat with pork meat is difficult to distinguish, especially for some ordinary people. This action is very detrimental and disturbing the local community, especially Muslims. At present, technological advances in the field of digital image processing are increasing rapidly, especially in food products. In general, this research was conducted in 2 (three) stages. The first stage, namely the stage of image data collection of pork and buffalo meat. The second stage, namely the classification of pork and buffalo meat images using image histogram analysis and the Gray Level Co-occurrence Matrix (GLCM) method based on the color and texture of the meat. In this study using the Red Green Blue (RGB) color image method and GLCM texture extraction, namely contrast, homogeneity, energy, and correlation. The study was conducted using 20 samples of meat images (10 images of pork and 10 images of buffalo meat, respectively). Based on the results of the research that has been done, it was found that the image of buffalo meat has a higher percentage value of the Red (R) color component when compared to the pork image, whereas the percentage value of the Green (G) and Blue (B) color components is lower when compared to the image pork. Then, if the value between pixels is not homogeneous (small homogeneity value), then the contrast value is large, and vice versa if the value between pixels is homogeneous (large homogeneity value) then the contrast value is small. The image of buffalo meat has a small homogeneity value compared to the image of pork, so the variation in intensity (contrast) in the image of buffalo meat is high.]]>
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