Deteksi Kondisi Terumbu Karang Menggunakan YOLO versi 8 pada Citra Bawah Laut Secara Real-Time
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1191Keywords:
Coral Reefs; Deep Learning; CNN; Computer Vision; Coral BleachingAbstract
Coral reef ecosystems play an important role in maintaining the balance of the marine environment and supporting the marine tourism sector. However, coral reef damage due to climate change, pollution, and human activities continues to increase, requiring efficient and sustainable monitoring methods. This study aims to develop a coral reef condition detection system based on the YOLOv8 method by utilizing real-time underwater imagery. The research dataset was obtained from the coral reef conservation area of ??Bahoi Village, West Likupang, North Sulawesi. The research stages include dataset collection, image preprocessing, data augmentation, object annotation, YOLOv8 model training, model performance evaluation, and web-based detection system implementation. Model evaluation was carried out using precision, recall, mean Average Precision (mAP), and confusion matrix metrics. The test results showed that the YOLOv8 model was able to detect coral reef objects with good performance, indicated by a precision value of 76.51%, recall of 98.57%, mAP50 of 86.78%, and mAP50-95 of 86.77%. Confusion matrix analysis showed that the model did not misclassify coral reef species, while a small number of errors occurred only in objects detected as background due to underwater environmental conditions such as water turbidity and light refraction. The results showed that YOLOv8 is effective for detecting and monitoring coral reef conditions automatically and in real time, thus potentially supporting conservation activities and sustainable marine ecosystem management.
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