Analisis Performa Metode YOLOv5-CNN Dalam Meningkatkan Deteksi Dan Pengenalan Ras Kelinci
Abstract
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.
Keywords: Object detection; Image classification; YOLOv5; EfficientNet-B0; Rabbit breeds
Abstrak
Identifikasi ras kelinci secara manual membutuhkan waktu dan rentan kesalahan, sehingga diperlukan sistem otomatis berbasis citra digital. Penelitian ini mengusulkan pendekatan YOLOv5-CNN untuk mendeteksi objek kelinci dan mengklasifikasikan rasnya secara otomatis. Tahap pertama menggunakan YOLOv5 untuk mendeteksi kelinci pada citra dan menghasilkan bounding box, kemudian citra hasil deteksi dijadikan masukan model Convolutional Neural Network (CNN) untuk klasifikasi ras. Pengujian dilakukan menggunakan dataset citra kelinci yang dibagi menjadi 70% data pelatihan, 10% data validasi, dan 20% data pengujian. Pada tahap pelatihan dan validasi, model menunjukkan kemampuan belajar yang stabil dalam mengenali pola visual antar ras. Selanjutnya, pengujian dilakukan pada 200 citra uji independen yang tidak digunakan selama proses pelatihan. Hasil evaluasi menunjukkan bahwa sistem kombinasi YOLOv5–CNN memperoleh akurasi sebesar 96% pada data uji. Temuan ini menunjukkan bahwa integrasi deteksi objek dan klasifikasi citra dalam satu alur pemrosesan dapat mendukung proses identifikasi ras kelinci secara otomatis berbasis citra digital.
Keywords
References
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