Classification of Beef and Pork with Deep Learning Approach
Abstract
Beef is one of the most consumed meats in Indonesia. However, the high price of beef has led to rogue traders mixing pork with beef. This condition occurs due to the lack of public knowledge about the difference between the two meats. To maintain food safety in Indonesia and especially in Riau province, the Livestock Service Office of Riau province conducts market surveys. There are several methods that are usually used to check the content of beef or pork, including Rapid Test Kit and Elisa. Both methods are time consuming and costly. One other solution that can be used is the artificial intelligence method, namely deep learning. In this research, a classification approach using deep learning is used to distinguish between beef and pork in the form of a web application. This research compares Convolutional Neural Network algorithm with Inception-V3 and Inception-Resnet-V2 architecture with hyperparameter optimization. From several experiments that have been carried out, the best model is the Inception-Resnet-V2 architecture with an experimental scenario using a learning rate of 0.001, and an optimizer Adam with an accuracy of 96.50%, Precision 96.48%, Recall 96.55% and F1-Score 96.50%. By using this model, web-based applications can be developed using the flask framework well and can perform classification accurately.
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References
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