Fuzzy Learning Vector Quantization for Classification of Mixed Meat
Image Based on Character of Color and Texture

Fuzzy Learning Vector Quantization for Classification of Mixed Meat
Image Based on Character of Color and Texture

Pork, Beef, FLVQ, GLCM, HSV, Image Processing

Beef consumption is quite high and expensive in the world. In Indonesia, beef prices are relatively expensive because the meat
supply chain from farmers to the market is quite long. The high demand for beef and the difficulty of obtaining meat are factors
in the high price of meat. This makes some meat traders cheat by mixing beef and pork (oplosan). Mixing beef and pork is
detrimental to beef consumers, especially those who are Muslim. In this paper, we proposed a new strategy for identifying beef,
pig, and mixed meat utilizing Fuzzy learning vector quantization (FLVQ) Based on the color and texture aspects of the meat.
The HSV (Hue saturation value) approach is used for color features, whereas the GLCM (Gray level co-occurrence matrix)
method is used for texture features. This study makes use of primary data collected from the Pasar Bawah Tourism and Cipuan
Market in Pekanbaru, Riau Province. The data set consists of 600 photos, 200 each of beef, pork, and mixed. Based on the test
scenario, the coefficient of fuzzyness and learning rate affect the accuracy of meat image identification. The proposed strategy
has succeeded in classifying pork, beef and mixed meat with the best percentage of accuracy results in theclasses of beef and
pork, beef and mixed, pork and mixed meat, respectively, at 100%, 97.5%, and 95%. This demonstrates that the proposed
strategy has succeeded in classifying the image of pork, beef, and mixed

Lidya Ningsih1
, Agus Buono2
, Mushthofa3
, Toto Haryanto4

Institut Pertanian Bogor

30-06-2022

Fajar bagus W

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Indonesia

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