IDENTIFICATION DIGITAL TOURIST PREFERENCES USING SENTIMENT ANALYSIS: DEALING IN POST-PANDEMIC COVID-19

Lutfia Septiningrum

doi:10.21831/natapraja.v10i2.59973

IDENTIFICATION DIGITAL TOURIST PREFERENCES USING SENTIMENT ANALYSIS: DEALING IN POST-PANDEMIC COVID-19

Lutfia Septiningrum, Universitas Negeri Yogyakarta, Indonesia

10.21831/natapraja.v10i2.59973

Abstract

This aims to determine the post-pandemic impact on tourism in Rembang based on the results of community study using social media review. To achieve this goal, this study conducted a sentiment analysis of public comments/reviews regarding travel preferences after the COVID-19 pandemic using the support vector machine (SVM) classification method for digital tourism. Digital tourism is one of the innovations used to maintain the existence of tourism during the COVID-19 pandemic. At present the Covid-19 cases are starting to tilt, the number of tourist visitors has increased because the Indonesian government has begun to relax policies regarding the prevention of COVID-19. This also happened in Rembang Regency. Based on the Minister of Tourism and Creative Economy Regulation Number 9 of 2021 concerning Guidelines for Sustainable Tourism Destinations, digital tourism will boost the regional economy. To measure people's views, the sentiment analysis method is used to see people's preferences. The results accuracy value of 83% and an AUC value of 82.3% show the level of confidence on community reviews/comments using several digital platforms such as Twitter, YouTube, and the Rembang local government website in the good category. The classification of community preferences is stated in 2 groups, the first is the people says post-pandemic digital tourism must be developed, and the second states that they have decided to come directly to tourist attractions after Covid-19 in Rembang regency. Research suggestions for local governments to continue to develop digital tourism and continue to improve real tourism facilities and infrastructure in Rembang regencyt, where the pathway will increase tourist visits and the economy of the community around tourist attractions.

Full Text:

PDF

References

Journal Article

P. K. Jain, R. Pamula, and G. Srivastava, “A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews,” Comput. Sci. Rev., 41(2), p. 100413, 2021, doi: 10.1016/j.cosrev.2021.100413.

S. Jardim and C. Mora, “Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning,” Procedia Comput. Sci., 196(2021), pp. 199–206, 2021, doi: 10.1016/j.procs.2021.12.006.

Y. C. Chang, C. H. Ku, and D. D. Le Nguyen, “Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry,” Inf. Manag., 59(2), p. 103587, 2022, doi: 10.1016/j.im.2021.103587.

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., 127(3), pp. 511–520, 2018, doi: 10.1016/j.procs.2018.01.150.

T. Sontayasara et al., “Twitter sentiment analysis of bangkok tourism during covid-19 pandemic using support vector machine algorithm,” J. Disaster Res., 16(1), pp. 24–30, 2021, doi: 10.20965/jdr. 2021.p0024.

I. P. Windasari and D. Eridani, “Sentiment analysis on travel destination in Indonesia,” in 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 17(2017), pp. 276–279, doi: 10.1109/ICITACEE.2017.8257717.

Y. H. Gu et al., “Sentiment analysis and visualization of Chinese tourism blogs and reviews,” Int. Conf. Electron. Inf. Commun. ICEIC 2018, 2018(1), pp. 1–4, 2018, doi: 10.23919/ELINFOCOM.2018.8330589.

V. Ramanathan and T. Meyyappan, “Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism,” in 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), 2019(4), pp. 1–5, doi: 10.1109/ICBDSC.2019.8645596.

F. Gorunescu, “Classification Performance Evaluation,” in Data Mining: Concepts, Models and Techniques, F. Gorunescu, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011(4), pp. 319–330.

Newspaper Article

Hasan, A. M. (26 Mei 2017). Sekarang Yogya bukan Kota Pelajar, tapi Kota Mal dan Hotel. Tirto.Id. Retrieved from https://tirto.id/sekarang-yogya-bukan kota-pelajar-tapi-kota-mal-dan-hotel-cpqZ

N. Ramadhian, “Menparekraf Sandiaga Sebut Devisa Sektor Pariwisata Menurun,” Kompas.com, 2021. Retrieved from https://travel.kompas.com/read/2021/08/19/153200127/menparekraf-sandiaga-sebut-devisa-sektor-pariwisata-menurun?page=all.

Ministry of Tourism and Creative Economy, “Tren Industri Pariwisata Tahun 2021,” Jakarta, 2021. Retrieved from https://www.kemenparekraf.go.id/pustaka/Buku-Tren-Pariwisata-2021

Regional Government of Rembang Regency, “Wabup Hanies : Lakukan Lompatan Promosi Wisata,” Regional Government of Rembang Regency Website, 2022. Retrieved from https://rembangkab.go.id/berita/wabup-hanies-lakukan-lompatan-promosi-wisata/

Website

Statistics of Rembang Regency, “Arrivals of Tourist (People), 2018-2020,” Statistics of Rembang Regency, 2022. Februari 12, 2023, retrieved from https://rembangkab.bps.go.id/indicator/16/141/1/wisatawan-yang-datang.html.

Statistics of Rembang Regency, “Development of Tourism Rembang in December 2020” Rembang, Februari 13, 2023. retrieved from https://rembangkab.bps.go.id/pressrelease/2021/04/26/335/perkembangan-pariwisata-rembang-bulan-desember-2020.html.

Statistics Indonesia, “Total Foreign Exchange of Tourism Sector (Billion US $), 2016-2018,” Retrieved from https://www.bps.go.id/publication/2022/02/25/0a2afea4fab72a5d052cb315/statistik-indonesia-2022.html

DOI: https://doi.org/10.21831/natapraja.v10i2.59973