<![CDATA[Fish has a very high nutritional content and is needed by the human body, such as a protein. With the increasing production, and need for consumption of goodand fresh fish, irresponsible sellers take advantage of this situation by selling fish that are not fit for consumption, such as fish that are not fresh (rotten), fish that contain chlorine and formalin which can be detrimental to consumers. The purpose of this study was to determine how accurate the identification of fish freshness quality using the extraction of Hue, Saturation, and Value (HSV) color characteristics. The research method used is K-Nearest Neighbor (KNN) and is classified into several parts, namely, data collection techniques, needs analysis, design, training, and then testing. The image sample data used in this study amounted to 240 images consisting of fresh and non-fresh fish images, which will then be divided into training data and test data. The training data sample amounted to 220 images with a division of 110 fresh fish images and 110 non-fresh fish images, while the test data sample totaled 20 images with a division of 10 fresh fish images and 10 non-fresh fish images. Analysis of color features is carried out on the gills and head or the area around the eyes of the fish using Red, Green, and Blue (RGB) colors, which will be converted into Hue, Saturation, and Value (HSV) color spaces for the extraction and training processes to obtain results. The results showed that the use of HSV color character extraction was successfully applied with an accuracy value in the training of 94.09% and testing of 90%]]>
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