<![CDATA[Rip currents are powerful ocean currents that can suddenly pull swimmer offshore and are often difficult to recognize visually. However, automatic monitoring technology for detecting rip currents is still limited, while small datasets often lead to overfitting problems and reduce detection accuracy. This study aims to optimize data augmentation parameters in YOLOv11 to improve the mean Average Precision (mAP) value and enable rip current detection even with limited data. The dataset was collected from Google Earth and aerial photographs from the Depok-Parangtritis coastline. Preprocessing includes manual labelling, cropping, and resizing to 640 x 640 pixels. Four augmentation techniques were applied, namely crop (0-10%), rotation (-10% to +10%), brightness adjustment (-10% to +10%), and 1 pixel blur using Roboflow. The dataset was split into 70% training and 30% validation. The YOLOv11 model was then trained and evaluated with precision, recall, and mAP metrics. Results show that data augmentation significantly improves model performance. Dataset 2 without augmentation achieved only 31.8% precision, 32.8% recall, and 23.8% mAP50, while the best model from a combination of the original Dataset 1 and the augmented Dataset 3 reached 90.6% precision, 85.7% recall, and 90.4% mAP50. The integration of YOLOv11 into a web application enables automatic detection in both images and videos with bounding box and confidence score. This study emphasizes the importance of visual variation in the dataset for improving the model generalization and provide a practical foundation of real-time coastal monitoring system.]]>
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