THE IDENTIFICATION OF FISH EYES IMAGERY TO DETERMINE THE QUALITY OF FISH MEAT BASED ON FUZZY LOGIC METHOD

Endi Permata, Ri Munarto, Reza Zembar Yupri

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The quality of sorting fish meat is required before processing because the fish whose quality is bad will affect the quality of the good fish if processed simultaneously. The classification process manually not only need a long time but also produces the inconsistent product quality. In this research, the selection of fish as an object because the fish which sold in modern and traditional market is dies, so that people do not know the quality of the fish that will be purchased. Because of that, this research tries to classification of the fish meat freshness automatically using HSV color features and Grayscale with classification method using fuzzy logic. Using fuzzy logic method is because it was considered to be able to resolve the problems are not linier. The fish image of the results of image acquisition will go through the process of preprocessing is a Contrast stretching processes, cropping and scaling, after that the fish eyes imagery will be through the feature extraction process, features that is used is a HSV color feature and Grayscale. The final process is classification using Fuzzy Logic method. The classification of the freshness of fish meat are fresh fish, fish that good enough and the fish in a bad condition are using samples of each 50 fish and using 3 types of fish. That are kembung, tongkol, and bandeng fish. The parts of fish that researched is eye. On the fish eye, the eye color is changes when fish is in a bad condition. From the results of the classification program using the GUI method can be determined the classification result process with accuracy of the overall system in kembung fish is 93%, bandeng fish is 89%, and tongkol fish is 88%. The classification result of the freshness of fish meat using a fish eye imagery based on fuzzy logic method has a good result.

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Referensi


Kuswadi, Son. (2007). Kendali Cerdas, Teori dan Aplikasi Praktisnya, Penerbit Andi

May, Z dan M. H. Amran. (2011). Automated Ripeness Assesment of Oil Palm Fruit Using RGB dan Fuzzy Logic

Rafael C.Gonzales dan Richard E. Wood, (2008), Digital Image Processing, Prentice Hall

Rafael C.Gonzales dan Richard E. Wood, (2008), Digital Image Processing Using MATLAB. Prentice Hall

Rinaldi Munir. Pengolahan Citra Digital (Computer vision & Image Processing). 2004, Informatika : Bandung

Widodo, Prabowo Pudjo dan Handayanto, Rahmadya Trias. (2012). Penerapan Soft Computing Dengan MATLAB. Rekayasa Sains

Yusra dan Yempita Efendi. Dasar-dasar Teknologi Hasil Perikanan.


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