Review Star Hotels Using Convolutional Neural Network

Authors

  • Edward Sze Universitas Pradita, Serpong, Banten, Indonesia
  • Handri Santoso Universitas Pradita, Serpong, Banten, Indonesia
  • Djarot Hindarto Universitas Pradita, Serpong, Banten, Indonesia

DOI:

10.33395/sinkron.v7i4.11836

Keywords:

Convolutional Neural Network, Image Classification, Review Image, Deep Learning, Dataset Hotel Review

Abstract

Currently the Deep Learning algorithm is developing very rapidly, where its application has helped a lot in individuals and businesses. One of its uses in conducting any review can be used this method. The review used in this case is to review five-star hotels. The hotel image is used as input for a review. So from the image of the hotel, it can be immediately known the level of the hotel. Usually the review is done using good sentences with compliments that tend to be positive sentiments. Meanwhile, sentences in the form of complaints tend to have negative sentiments. This study does not use sentences in conducting a review and uses a simple method in conducting the review process. The use of images as input is classified into five classes, namely one-star hotel class, two-star hotel class, three-star hotel class, four-star hotel class and five-star hotel class. The purpose of this research is to conduct a review on five-star hotels with image as input and hotel review as the output of the Deep Learning algorithm process. Deep Learning algorithm process using Convolutional Neural Network (CNN). The datasets used are public datasets and private datasets. The use of these datasets is a way to get better training model results. So that the accuracy in reviewing the image becomes better. The results of this study resulted in an accuracy reaching 98.48%, while for Loss it reached 0.0554.

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How to Cite

Sze, E., Santoso, H. ., & Hindarto, D. . (2022). Review Star Hotels Using Convolutional Neural Network. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2469-2477. https://doi.org/10.33395/sinkron.v7i4.11836

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