Compare VGG19, ResNet50, Inception-V3 for Review Food Rating
Open Access
- 1 April 2022
- journal article
- Published by Politeknik Ganesha in sinkron
- Vol. 7 (2), 845-494
- https://doi.org/10.33395/sinkron.v7i2.11383
Abstract
The food industry is undergoing a phase of very good improvement, where business actors are experiencing very rapid growth. Creative ideas are many and creative on several social media. When an online business is growing rapidly, many managers in the food sector market their products through online media. So it is quite easy for customers to place orders via mobile. Especially during the COVID-19 pandemic, where a ban on gatherings has become a government recommendation for many food business actors to sell online. Since then, almost all food industry players have made their sales online. There are many advantages of doing business online. The food served is in the form of pictures that attract market visitors so that it can create its own charm. Food is just a click away to order, and the order comes. No need to queue and everything has been delivered to the ordered goods. After the ordered goods arrive, the customer reviews the food or drink. Because customer reviews are the result of customer ratings. The result of the review is one of the sentiment analyses, which in this study is in the form of a review of the images available on the display marketplace. The method used is Convolutional Neural Network. The dataset will be extracted features and classifications. The research will do a comparison using VGG19, ResNet50, and Inception-V3. Where the accuracy of VGG19 = 96.86; Resnet50 : 97.29; Inception_v3 : 97.57.Keywords
This publication has 24 references indexed in Scilit:
- Arabic language sentiment analysis on health servicesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet ModelPublished by Association for Computing Machinery (ACM) ,2016
- Snap, Eat, RepEatPublished by Association for Computing Machinery (ACM) ,2016
- Retrieval and classification of food imagesComputers in Biology and Medicine, 2016
- Framework for Sentiment Analysis of Arabic TextPublished by Association for Computing Machinery (ACM) ,2016
- Sentiment Analysis of Review Datasets Using Naïve Bayes‘ and K-NN ClassifierInternational Journal of Information Engineering and Electronic Business(ijieeb), 2016
- Rethinking the Inception Architecture for Computer VisionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Fine-Grained Image Classification by Exploring Bipartite-Graph LabelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Political Sentiment Analysis Using Twitter DataPublished by Association for Computing Machinery (ACM) ,2016
- BiSAL – A bilingual sentiment analysis lexicon to analyze Dark Web forums for cyber securityDigital Investigation, 2015