Implementasi Pengolahan Citra dengan Menggunakan Metode K-Nearest Neighbor Untuk Mengetahui Daging Ayam Busuk dan Daging Ayam Segar
DOI:
https://doi.org/10.30865/jurikom.v8i2.2818Keywords:
Meat, GLCM, K-Nearest Neighbor, Classification, AccuracyAbstract
Animal flesh is animal muscle composed of very small fibers, each consisting of elongated cells held together by connective tissue, making bonds that generally contain clear flesh, fat, blood, and nerve fibers. Increased consumption of animal meat, it is no wonder meat consumption is found in many markets. Basically meat - meat consumption is sold by traders according to its type. Types of meat that are often consumed are rotten chicken meat and fresh chicken meat. Many consumers do not like the act of mixing meat consumption because it is difficult to distinguish by consumers. This meat mixer is very detrimental to consumers. This study discusses rotten chicken meat and rotten chicken meat using the K-Nearest Neighbor method. The image material used in this study was rotten chicken meat and fresh chicken meat. The original image sample is saved in RGB (Red Green Blue). The first step is resizing the image to 5x5 pixels and turning grayish (Grayscale). To question the characteristics of the image, the Gray Level Co-Occurence Matrix (GLCM) method is used. The K-Nearest Neighbor method in this study uses the value k = 3. The angle orientation used is 0o. The highest accuracy obtained in testing the image of fresh chicken meat.
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