<![CDATA[Freshwater fish are an important commodity in the fishing industry that requires an accurate classification system. This study aims to develop a freshwater fish classification system using the Convolutional Neural Network (CNN) algorithm with AlexNet architecture, as well as applying data augmentation techniques to improve model accuracy. The dataset used consists of 488 images of five types of freshwater fish, namely catfish, baung fish, tapah fish, juaro fish, and patin fish, which were then augmented into 68,400 images. The model was trained using the Adam optimizer, with a batch size of 16, a learning rate of 1e-5, and 200 epochs. The results of the experiment show that the model achieved a training accuracy of 71.09%, a validation accuracy of 85.00%, and a testing accuracy of 80.29%. Precision reached 0.8310, Recall 0.7909, and F1-score 0.7912, indicating the model's excellent performance in classifying freshwater fish species. This research is expected to support the development of an automatic classification system for the freshwater fisheries industry]]>
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