Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture
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 m...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4067 |
| _version_ | 1797334587676819456 |
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| author | Lidya Ningsih Agus Buono Mushthofa Toto Haryanto |
| author_facet | Lidya Ningsih Agus Buono Mushthofa Toto Haryanto |
| author_sort | Lidya Ningsih |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T08:24:12Z |
| format | Article |
| id | doaj.art-2a674d418c254dd5964dd87e00bdc1e1 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T08:24:12Z |
| publishDate | 2022-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-2a674d418c254dd5964dd87e00bdc1e12024-02-02T05:04:43ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-06-016342142910.29207/resti.v6i3.40674067Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and TextureLidya Ningsih0Agus Buono1Mushthofa2Toto Haryanto3Institut Pertanian BogorInstitut Pertanian BogorInstitut Pertanian BogorInstitut Pertanian BogorBeef 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.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4067pork, beef, flvq, glcm, hsv, image processing |
| spellingShingle | Lidya Ningsih Agus Buono Mushthofa Toto Haryanto Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) pork, beef, flvq, glcm, hsv, image processing |
| title | Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture |
| title_full | Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture |
| title_fullStr | Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture |
| title_full_unstemmed | Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture |
| title_short | Fuzzy Learning Vector Quantization for Classification of Mixed Meat Image Based on Character of Color and Texture |
| title_sort | fuzzy learning vector quantization for classification of mixed meat image based on character of color and texture |
| topic | pork, beef, flvq, glcm, hsv, image processing |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4067 |
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