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

CAROLINE, CYNTHIA and Nurmaini, Siti (2021) KLASIFIKASI OBJEK MAKANAN DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

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

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Makanan, Pengenalan Gambar, Deep Learning, Jaringan Saraf Konvolusional
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 9619 not found.
Date Deposited:	08 Jan 2021 02:58
Last Modified:	08 Jan 2021 02:58
URI:	http://repository.unsri.ac.id/id/eprint/39423

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