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KLASIFIKASI OBJEK MAKANAN DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

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Social media are popular platforms frequently used to share mementos and as a means of marketing strategy, especially by the food industry. Unfortunately, most food photos in social media are not labelled or properly explained, and can lead to confusion by the user. To combat this problem, a novel solution was developed to detect food photos swiftly and automatically with three Convolutional Neural Network (CNN) architectures such as AlexNet, Inception V3, and Resnet 50. Two image processing techniques were implemented, namely Histogram Equalisation and data augmentation. The Food-101 dataset was used, which incorporated a range of diverse food images from the internet and social media. This study revealed that Inception V3 with data augmentation was the best model. Its accuracy was 99.03%, whereas the precision, recall, and F1-score was 99%. Moreover, it had an error rate of 0.005, false positive rate of 0, and false negative rate of 0.013. In addition, this paper demonstrated that the worst model was AlexNet with Histogram Equalization with an accuracy, precision, recall and F1-score of 23%. Furthermore, the error, false positive, and false negative rates were 0.03, 0.015, and 0.76, respectively. Keywords : Food Classification, Image Recognition, Deep Learning, Convolutional Neural Network

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Inventory Code Barcode Call Number Location Status
2107002766T39423T394232021Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T394232021
Publisher
Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xx, 177 hlm. : ilus. ; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.607
Content Type
Text
Media Type
unmediated
Carrier Type
unspecified
Edition
-
Subject(s)
Komputer grafis
Prodi Sistem Komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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