Analisis Performa Metode YOLOv5-CNN Dalam Meningkatkan Deteksi Dan Pengenalan Ras Kelinci

Manual identification of rabbit breeds is time-consuming and error-prone, requiring an automated system based on digital images. This study proposes the YOLOv5-CNN approach to automatically detect rabbit objects and classify their breeds. The first stage uses YOLOv5 to detect rabbits in images and generate bounding boxes. The detected images are then used as input for a Convolutional Neural Network (CNN) model for breed classification. Testing was conducted using a rabbit image dataset divided into 70% training data, 10% validation data, and 20% testing data. In the training and validation stages, the model demonstrated stable learning capabilities in recognizing visual patterns between breeds. Next, testing was conducted on 200 independent test images not used during the training process. The evaluation results showed that the YOLOv5-CNN combination system achieved 96% accuracy on the test data. These findings demonstrate that the integration of object detection and image classification in a single processing pipeline can support automatic rabbit breed identification based on digital images.