A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images

Zulfikar, Wildan Budiawan and Angelyna, Angelyna and Irfan, Mohamad and Rialdy Atmadja, Aldy and Jumadi, Jumadi (2025) A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images. JOIV (International Journal on Informatic Visualization ), 9 (2). pp. 568-574. ISSN 2549-9904

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Abstract

Abstract—There are 87.2% of the Muslim population in Indonesia, which makes Indonesia one of the countries with the largest Muslim population in the world. As a Muslim, it is supposed to carry out and stay away from the commands that Allah SWT commands, one of which is in QS. Al-maidah:3, one of the commands in the verse is not to consume haram food such as pork. Even so, it turns out that many traders in Indonesia still cheat to get more significant profits, namely by counterfeiting beef and pork. The lack of public knowledge supports this situation to differentiate between the two types of meat. Therefore, the classification process is used to distinguish the two kinds of meat using the convolutional neural network approach with VGG16 with several preprocessing stages. Two primary stages are used during the preprocessing stage: scaling and contrast enhancement. The VGG16 algorithm gets very good results by getting an accuracy value of 99.6% of the test results using 4,500 images for training data and 500 images for testing data. To compare the effectiveness of these techniques, it is recommended to use alternative CNN architectures, such as mobilNet, ResNet, and GoogleNet. More investigation is also required to gather more varied datasets, enabling the ultimate goal to achieve the best possible categorization, even when using cell phone cameras or with dim or fuzzy photos.

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