<![CDATA[Manual identification of fish species at fish auction sites (TPI) was often time-consuming and prone to inconsistencies, which affected economic valuation and data recording accuracy. This study proposed an automated fish classification system to address these challenges using the K-Nearest Neighbors (KNN) method. The system was designed to assist the fish identification process in the Java Sea, with a case study conducted at the Karanganyar Fish Auction Site. The proposed approach employed computer vision techniques, beginning with image pre-processing steps such as segmentation and cropping to isolate fish objects. Subsequently, two complementary feature extraction methods were combined to obtain a robust representation of each fish image: Hu Moments for capturing holistic shape features that are invariant to scale and rotation, and Local Binary Pattern (LBP) for extracting detailed surface texture information. This hybrid feature representation provided a comprehensive descriptor for every fish instance. The dataset consisted of 1,000 images categorized into 10 main fish species (e.g., tongkol, bawal, and others). Model training and hyperparameter optimization were performed using a k-fold cross-validation scheme, followed by an 80:20 train-test evaluation. The experimental results demonstrated that the KNN model with the optimal k value achieved an overall classification accuracy of 98.50% on the unseen test set. These findings indicated that the integration of Hu Moments and LBP features was highly effective in distinguishing fish species and showed strong potential for practical implementation as a fast, objective, and reliable identification tool at fish auction sites such as Karanganyar Fish Auction Site]]>
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