<![CDATA[Marine animals are very susceptible to decay. The traditional method that is often used to distinguish the freshness of squid by local people is from the body color and smell of the squid. This method is very simple but has many shortcomings in distinguishing freshness from squid. The drawback of this method lies in the understanding and level of accuracy of each person who is different. So it is necessary to create a system that can distinguish the freshness level of squid automatically only from the image of the squid. In this study, a system model was developed that can classify the freshness level of squid using the Support Vector Mahine (SVM) method. The GLCM and histogram methods as well as the HSI color space are used for texture and color feature extraction. This study uses three types of classification. The total data used in this study are 360 body images of squid that have been cropped and resized by 128 x 128 pixels for the treatment type class. The total data for the freshness class with three types of classes is 495 and the total data for the freshness class with two types of classes is 330. In this study, the process of cropping, augmentation, resizing and conversion of color space in the dataset was carried out. The distribution of training data and test data is 70:30. The highest accuracy obtained is 67.75%.]]>
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