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The abundant biodiversity of coral reefs in Indonesian waters is a valuableasset that needs to be preserved. Rapid climate change and uncontrolled humanactivities have led to the degradation of coral reef ecosystems, includingcoral bleaching, which is a critical indicator of coral health conditions.Therefore, this research aims to develop an accurate classification model todistinguish between healthy corals and corals experiencing bleaching. Thisstudy utilizes a specialized dataset consisting of 923 images collected fromFlickr using the Flickr API. The dataset comprises two distinct classes:healthy corals (438 images) and bleached corals (485 images). These images havebeen resized to a maximum of 300 pixels in width or height, whichever islarger, to maintain consistent sizes across the dataset.  The method employed in this research involves the use of machine learningmodels, particularly convolutional neural networks (CNN), to recognize anddifferentiate visual patterns associated with healthy and bleached corals. Inthis context, the dataset can be used to train and test various classificationmodels to achieve optimal results. By leveraging the ResNet model, it was foundthat a from-scratch ResNet model can outperform pretrained models in terms ofprecision and accuracy. The success in developing accurate classificationmodels will greatly benefit researchers and marine biologists in gaining abetter understanding of coral reef health. These models can also be employed tomonitor changes in the coral reef environment, thereby making a significantcontribution to conservation and ecosystem restoration efforts that havefar-reaching impacts on life.

Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra