<![CDATA[The increase in counterfeit beef sales is in line with the growing demand for meat in Indonesia. Counterfeit meat, namely mixed beef and pork and pure pork sold as beef, can be distinguished using image classification. This study classifies pork, mixed, and beef using the Convolutional Neural Network (CNN) model of the EfficientNet-B0 architecture. This study uses the image augmentation method to augment the image with the aim of improving classification accuracy. The total original image is 900, while the total augmented image is 9000. The image data is divided using two data division ratios, namely 80:20 and 90:10. The highest classification accuracy results were obtained by a model using augmented images and a data division ratio of 90:10, with a combination of Adamax hyperparameter optimizer, Swish hidden activation, and a learning rate of 0.1, with an accuracy of 97.11%, precision of 97.14%, recall of 97.11%, and F1-Score of 97.11%. Meanwhile, the highest accuracy of the model using the original image is achieved by the model using a 90:10 division ratio with a combination of hyperparameter optimizer Adamax, hidden activation ReLU, and learning rate 0.01 with the results of accuracy 96.78%, precision 96.92%, recall 96.78%, and F1-Score 96.78%. The results show that the use of image augmentation methods can improve classification accuracy.]]>
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