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Sarah Lasniari
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering

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Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 3, No 4 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4167

Abstract

Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.
Co-Authors Febi Yanto Jasril Jasril Muhammad Affandes Suwanto Sanjaya