Application of YOLOv8 Algorithm for Coral Reef Disease Detection as an Effort to Prevent Marine Habitat Damage in Batam

Research in 2019 in Batam City showed that out of 19 coral reef fisheries support facilities, 16 were declared not good. Coral reef damage increased from 36.28% to 39.44%. This is due to the threat of coral reef damage due to international shipping lane areas, human activities such as destructive fishing, pollution, sedimentation, and global warming. These threats can cause coral diseases such as black band disease (BBD), brown band disease (BrB), Bleaching Coral, and yellow band disease (YBD). The Underwater Photo Transect (UPT) method collects data in the field in the form of underwater photos and analyzes them to obtain quantitative data. This method has a weakness, namely the low level of accuracy in detecting coral reef diseases. This study proposes coral reef disease detection using the YOLO model YOLO8l, YOLO8x, and YOLO8m. The results of the model evaluation test with a threshold value of 0.5 to 0.95 against the test data show that the three models can detect coral reef diseases with an accuracy of 99%. These results prove that the YOLOv8 model in this study is suitable for the real-time detection of coral reef diseases to replace the Underwater Photo Transect (UPT) method, which has low accuracy. Applying the YOLOv8 method will help Prevent Marine Habitat Damage in Batam City.