<![CDATA[Coral reefs are marine ecosystems that are highly vulnerable to damage and require regular monitoring of their health conditions. However, the manual classification process of coral reef health tends to be time-consuming. Therefore, this research aims to develop an application that implements a transfer learning model for classifying coral reef health based on digital images. This study utilizes three pretrained model architectures: DenseNet121, MobileNetV2, and EfficientNet-B0. Each model is trained and evaluated to measure its performance in classifying coral reef images. The best-performing model, DenseNet121, is then integrated into a mobile application for real-time classification. The evaluation results show that DenseNet121 achieved the highest accuracy compared to MobileNetV2 and EfficientNet-B0. The training data accuracy of DenseNet121 reached 98.80%, and the testing data accuracy was 98.25%.]]>
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