<![CDATA[Abstract. The increasing demand for fresh salmon in Indonesia, despite it not being a producing country, poses challenges in maintaining product quality during distribution. Freshness is a critical factor due to the fish's high susceptibility to spoilage, which can lead to health risks and economic losses. Purpose: Traditional inspection methods are inefficient for large-scale operations. Therefore, this study aims to develop an efficient and accurate classification model for fresh and infected salmon using ensemble learning based on Convolutional Neural Networks (CNN), particularly ResNet50 and EfficientNetV2 architectures. Methods/Study design/approach: This research employs a quantitative approach using the SalmonScan dataset, consisting of 1,208 images divided into two classes: fresh and infected salmon. The data underwent preprocessing, including resizing and normalization. Two deep learning architectures, ResNet50 and EfficientNetV2, were applied using the transfer learning method. These models were then combined using ensemble learning with a concatenation strategy to enhance performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score, based on the confusion matrix. Results/Findings: Individual testing of ResNet50 and EfficientNetV2 models achieved high performance, but the ensemble of both architectures yielded the best results. The combined model achieved an accuracy of 98.33%, outperforming other models used in the experiment. These results indicate that the ensemble approach successfully improves the model's capability to classify salmon freshness and infection conditions. Novelty/Originality/Value: This study presents a novel ensemble approach that integrates ResNet50 and EfficientNetV2 for classifying salmon freshness. Unlike previous works that utilized either single models or more computationally expensive ensemble methods with multiple architectures, this study provides a balanced, computationally efficient solution with high accuracy. The proposed method demonstrates potential for scalable applications in fish quality assessment systems, supporting food safety and sustainability in the fisheries industry.]]>
