Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Authors

  • Andrew Andrew Universitas Pradita
  • Handri Santoso Universitas Pradita

DOI:

10.33395/sinkron.v7i2.11383

Keywords:

Sentiment Analysis, Food Rating, Image Review, Convolutional Neural Network, Dataset

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.

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How to Cite

Andrew, A., & Santoso, H. . (2022). Compare VGG19, ResNet50, Inception-V3 for Review Food Rating. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 845-494. https://doi.org/10.33395/sinkron.v7i2.11383