<![CDATA[Indonesia is a maritime country where fish is the most widely extracted and consumed marine natural resource, one of which is snapper. Snapper contains high protein. Therefore, it is suitable for health. Red snapper or Lutjanus campechanus is one economical fish with a broad market share. Red snapper is a demersal fish group that ranks third with the most exported commodities after tuna and shrimp. In addition, snapper is one of the most common consumption fish in Indonesia. Therefore, the community needs to be able to identify the freshness of the fish. Fish freshness detection is done manually by touching the fish's body, eyes, and gills. However, this can cause accidental damage to the fish parts, which will be very detrimental. Several studies on identifying fish freshness explain that the VGGNet-16 Architecture on the Convolutional Neural Network algorithm is superior in its modeling performance. This research uses a different fish object, a red snapper object, with two different architectures from several previous studies, namely the Le-Net15 and VGGNet-16 architecture. This research focuses on the eye image carried out through the pre-processing data stage by cutting the fish body, followed by augmentation to reproduce the image data without losing its essence before training the dataset. The model will be trained using the Adam optimization method with very fresh and not fresh predictions. The experimental results of the classification of two classes of red snapper freshness using 600 fish images show that VGGNet-16 achieves the best performance compared to the LeNet-5 architecture, where the classification accuracy reaches 98.40%.]]>
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