<![CDATA[Digital image-based chicken species classification plays a crucial role in supporting the identification process in the livestock sector. However, the similarity in visual characteristics between chicken species makes the manual classification process inefficient and error-prone. This study aims to develop a chicken species classification model using the Ensemble Soft Voting method by combining two Convolutional Neural Network (CNN) architectures, namely MobileNetV2 and EfficientNet-B0. The dataset used consists of 1,500 images from five chicken classes: Bangkok Chicken, Broiler Chicken, Free-Range Chicken, Bantam Chicken, and Laying Hen. The research stages include preprocessing, data augmentation, transfer learning, model training, and combining prediction probabilities using the Soft Voting method. Evaluation is carried out using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix metrics. The results showed that MobileNetV2 achieved an accuracy of 97.18%, EfficientNet-B0 achieved 96.48%, while the Ensemble Soft Voting method produced the best performance with an accuracy of 97.89%, a precision of 97.96%, a recall of 97.89%, and an F1-score of 97.88%. These results indicate that the Ensemble Soft Voting method is able to improve classification accuracy and stability compared to using a single model, so it has the potential to be applied as a solution for digital image-based chicken species identification.]]>
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