Cases of mixing beef and pork are still happening today. The increasing demand for beef causes many traders to mix meat to gain more profit. Distinguishing beef and pork can be done by sight and smell, but still has weaknesses. This study uses Deep Learning method for image classification with Convolutional Neural Network architecture EfficientNet-B0. The amount of data is 3,000 images which are divided into 3 classes, beef, pork, and mixed meat. This study uses original image data and image data of Contrast Limited Adaptive Histogram Equalization. The data is divided by the ratio of training data and test data of 80:20. The results of testing the model with the confusion matrix show the highest classification performance with 95.17% accuracy, 92.72% precision, 95.5% recall, and 94.09% f1 score, in the original image data with the use of neurons in the first dense amounting to 256, 32 batch size, 0.01 learning rate, and Adam's optimizer