Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02043
Number of page(s) 14
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802043
Published online 17 November 2023
  1. B. Liu-Lastres, H. Wen, and F. Okumus, “Exploring the impacts of internal crisis communication on tourism employees insights from a mixed-methods study,” Tour. Manag., vol. 100, no. April 2023, p. 104796, 2024, doi: 10.1016/j.tourman.2023.104796. [CrossRef] [Google Scholar]
  2. F. Hamzah and H. Hermawan, “Evaluasi Dampak Pariwisata Terhadap Sosial Ekonomi Masyarakat Lokal,” J. Pariwisata, vol. 5, no. 3, pp. 195–202, 2018, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/jp [Google Scholar]
  3. I. A. made ) Tikson (2001, dalam Imade Agung Wardana, “Jurnal Destinasi Pariwisata,” vol. 8, no. 1, pp. 78–84, 2018. [Google Scholar]
  4. B. Al sari et al., “Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00568-5. [CrossRef] [Google Scholar]
  5. I. G. M. Darmawiguna, G. A. Pradnyana, and G. S. Santyadiputra, “The Development of Integrated Bali Tourism Information Portal using Web Scrapping and Clustering Methods,” J. Phys. Conf. Ser., vol. 1165, no. 1, 2019, doi: 10.1088/1742-6596/1165/1/012010. [Google Scholar]
  6. S. S. Salim and J. Mayary, “Analisis Sentimen Pengguna Twitter Terhadap Dompet Elektronik Dengan Metode Lexicon Based Dan K – Nearest Neighbor,” J. Ilm. Inform. Komput., vol. 25, no. 1, pp. 1–17, 2020, doi: 10.35760/ik.2020.v25i1.2411. [Google Scholar]
  7. R. Mahendrajaya, G. A. Buntoro, and M. B. Setyawan, “Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based Dan Support Vector Machine,” Komputek, vol. 3, no. 2, p. 52, 2019, doi: 10.24269/jkt.v3i2.270. [CrossRef] [Google Scholar]
  8. N. Leelawat et al., “Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning,” Heliyon, vol. 8, no. 10, p. e10894, 2022, doi: 10.1016/j.heliyon.2022.e10894. [CrossRef] [PubMed] [Google Scholar]
  9. D. Indra, J. Endro, and W. Amien, “Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction,” vol. 01, pp. 1–9, 2023, doi: 10.21456/vol13iss1pp1-9. [Google Scholar]
  10. U. A. Essaâdi, L. Lirosa, Y. A. L. Amrania, M. Lazaarb, and T. El, “Random Forest dan Support Vector Machine berbasis Hybrid Pendekatan Analisis Sentimen Analisis Pendekatan Sentimen,” vol. d, pp. 1–10, 2018. [Google Scholar]
  11. Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, “Random forest and support vector machine based hybrid approach to sentiment analysis,” Procedia Comput. Sci., vol. 127, pp. 511–520, 2018, doi: 10.1016/j.procs.2018.01.150. [CrossRef] [Google Scholar]

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