Image Classification of Beef and Pork Using Convolutional Neural Network Architecture EfficienNet-B1

Isnan Mellian Ramadhan, Jasril - Jasril, Suwanto Sanjaya, Febi Yanto, Fadhilah Syafria


The increasing demand for beef has made many meat traders mix beef with pork to get more profit. Mixing beef and pork is harmful, especially for Muslims. In this study, the EfficientNet-B1 Convolutional Neural Network (CNN) approach was used to classify beef and pork. Experiments were conducted to compare accuracy using original data (without data augmentation) and with data augmentation. The data augmentation techniques used are rotation and horizontal flip. The total dataset after the data augmentation process is 3000 images. Many different settings were tested, including learning rates (0.00001, 0.0001, 0.001, 0.01, 0.1), batch size (32, 64), and optimizer (Adam, Adamax). After testing the Confusion Matrix, the highest accuracy results were obtained using data augmentation with a batch size of 32 of 98%. Meanwhile, those without data augmentation were 96%


Convolutional Neural Network, Classification Beef and Pork, EfficienNet-B1, Data Augmentation, Learning Rates, Batch size, Optimizer (Adam, Adamax)

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