Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan
  Klasifikasi Citra
Fadhil Muhammad Alif Bintang Elfandra Iqbal Pahlevi Amin Alfan Farizki Wicaksono
The abundant biodiversity of coral reefs in Indonesian waters is a valuable
asset that needs to be preserved. Rapid climate change and uncontrolled human
activities have led to the degradation of coral reef ecosystems, including
coral bleaching, which is a critical indicator of coral health conditions.
Therefore, this research aims to develop an accurate classification model to
distinguish between healthy corals and corals experiencing bleaching. This
study utilizes a specialized dataset consisting of 923 images collected from
Flickr using the Flickr API. The dataset comprises two distinct classes:
healthy corals (438 images) and bleached corals (485 images). These images have
been resized to a maximum of 300 pixels in width or height, whichever is
larger, to maintain consistent sizes across the dataset.
  The method employed in this research involves the use of machine learning
models, particularly convolutional neural networks (CNN), to recognize and
differentiate visual patterns associated with healthy and bleached corals. In
this context, the dataset can be used to train and test various classification
models to achieve optimal results. By leveraging the ResNet model, it was found
that a from-scratch ResNet model can outperform pretrained models in terms of
precision and accuracy. The success in developing accurate classification
models will greatly benefit researchers and marine biologists in gaining a
better understanding of coral reef health. These models can also be employed to
monitor changes in the coral reef environment, thereby making a significant
contribution to conservation and ecosystem restoration efforts that have
far-reaching impacts on life.