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.

GS Cited Analysis

Downloads

Download data is not yet available.

References

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

Chandrasekaran, G., Antoanela, N., Andrei, G., Monica, C., & Hemanth, J. (2022). Visual Sentiment Analysis Using Deep Learning Models with Social Media Data. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12031030

Doshi, U., Barot, V., & Gavhane, S. (2020). Emotion detection and sentiment analysis of static images. 2020 International Conference on Convergence to Digital World - Quo Vadis, ICCDW 2020, Iccdw. https://doi.org/10.1109/ICCDW45521.2020.9318713

Du, R., Liu, W., Fu, X., Meng, L., & Liu, Z. (2022). Random noise attenuation via convolutional neural network in seismic datasets. Alexandria Engineering Journal, 61(12), 9901–9909. https://doi.org/10.1016/j.aej.2022.03.008

Gao, L., Xie, R. H., Xiao, L. Z., Wang, S., & Xu, C. Y. (2022). Identification of low-resistivity-low-contrast pay zones in the feature space with a multi-layer perceptron based on conventional well log data. Petroleum Science, 19(2), 570–580. https://doi.org/10.1016/j.petsci.2021.12.012

Gherkar, Y., Gujar, P., Gaziyani, A., & Kadu, S. (2022). Sentiment Analysis of Images using Machine Learning Techniques Yash. 03029, 1–6.

Hassan, S. Z., Ahmad, K., Hicks, S., Halvorsen, P., Al-Fuqaha, A., Conci, N., & Riegler, M. (2022). Visual Sentiment Analysis from Disaster Images in Social Media. Sensors, 22(10), 1–21. https://doi.org/10.3390/s22103628

Hindarto, D., Indrajit, R. E., & Dazki, E. (2021). Sustainability of Implementing Enterprise Architecture in the Solar Power Generation Manufacturing Industry. Sinkron, 6(1), 13–24. https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11115

Kim, H., Jung, W. K., Park, Y. C., Lee, J. W., & Ahn, S. H. (2022). Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques. Expert Systems with Applications, 188, 116014. https://doi.org/10.1016/j.eswa.2021.116014

Kim, J., & Park, H. (2022). Limited Discriminator GAN using explainable AI model for overfitting problem. ICT Express, xxxx. https://doi.org/10.1016/j.icte.2021.12.014

McCombe, K. D., Craig, S. G., Viratham Pulsawatdi, A., Quezada-Marín, J. I., Hagan, M., Rajendran, S., Humphries, M. P., Bingham, V., Salto-Tellez, M., Gault, R., & James, J. A. (2021). HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Computational and Structural Biotechnology Journal, 19, 4840–4853. https://doi.org/10.1016/j.csbj.2021.08.033

Mukhopadhyay, A. K., Majumder, S., & Chakrabarti, I. (2022). Systematic realization of a fully connected deep and convolutional neural network architecture on a field programmable gate array. Computers and Electrical Engineering, 97(October 2020), 107628. https://doi.org/10.1016/j.compeleceng.2021.107628

Opěla, P., Schindler, I., Kawulok, P., Kawulok, R., Rusz, S., & Navrátil, H. (2021). On various multi-layer perceptron and radial basis function based artificial neural networks in the process of a hot flow curve description. Journal of Materials Research and Technology, 14, 1837–1847. https://doi.org/10.1016/j.jmrt.2021.07.100

Rodriguez-Martinez, I., Lafuente, J., Santiago, R. H. N., Dimuro, G. P., Herrera, F., & Bustince, H. (2022). Replacing pooling functions in Convolutional Neural Networks by linear combinations of increasing functions. Neural Networks, 152, 380–393. https://doi.org/10.1016/j.neunet.2022.04.028

Tabinda Kokab, S., Asghar, S., & Naz, S. (2022). Transformer-based deep learning models for the sentiment analysis of social media data. Array, 14(April), 100157. https://doi.org/10.1016/j.array.2022.100157

Valentino, F., Cenggoro, T. W., & Pardamean, B. (2021). A Design of Deep Learning Experimentation for Fruit Freshness Detection. IOP Conference Series: Earth and Environmental Science, 794(1). https://doi.org/10.1088/1755-1315/794/1/012110

Yeşilmen, S., & Tatar, B. (2022). Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production. Case Studies in Construction Materials, 17(April). https://doi.org/10.1016/j.cscm.2022.e01372

Downloads


Crossmark Updates

How to Cite

Sze, E., Santoso, H. ., & Hindarto, D. . (2022). Review Star Hotels Using Convolutional Neural Network. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(4), 2469-2477. https://doi.org/10.33395/sinkron.v7i4.11836

Most read articles by the same author(s)

<< < 1 2 3 4 5 > >>