Analisis sentimen ulasan Google Maps pada daya tarik wisata Belitung Timur: Pemanfaatan Big Data untuk rekomendasi pengembangan pariwisata daerah

Authors

DOI:

https://doi.org/10.17509/jithor.v9i1.90635

Keywords:

Sentiment Analysis, Big Data, Tourism, Google Maps, East Belitung

Abstract

Tourism plays an important role in economic growth, cultural identity, and community welfare. East Belitung Regency has diverse tourism potential; however, data from 2020–2024 indicate fluctuations in visitor numbers due to the COVID-19 pandemic and management issues related to tourism attractions. This study analyzes tourist perceptions of nine major tourism attractions through Google Maps reviews, utilizing big data to generate regional tourism policy recommendations. Data were collected through SerpAPI scraping for the 2020–2025 period, yielding 1,933 valid reviews analyzed using a lexicon-based sentiment analysis and word cloud visualization in Jupyter Notebook. The results show that neutral sentiment dominates (50.23%), followed by positive (44.07%) and negative (5.7%). Vihara Dewi Kwan Im and Pantai Serdang emerge as leading tourism attractions, while Kampoeng Fifi and the Replika SD Laskar Pelangi are dominated by neutral reviews, indicating the need for attraction innovation. Meanwhile, the Museum Andrea Hirata and Pantai Nyiur Melambai record relatively higher negative reviews related to pricing, facilities, and cleanliness. Word cloud analysis confirms that natural beauty, spiritual value, and cultural icons are the main strengths, while cleanliness and facility management remain key challenges. These findings highlight the importance of online review big data as an early warning system for adaptive and sustainable tourism policy. 

References

Badan Pusat Statistik. (2022). Peraturan Badan Pusat Statistik Nomor 3 Tahun 2022 tentang Indeks Pembangunan Statistik (IPS). BPS. https://ppid.bps.go.id/upload/doc/Peraturan_Badan_Pusat_Statistik_Nomor_3_Tahun_2022_1679381490.pdf

Bagherzadeh, S., Shokouhyar, S., & Jahani, H. (2021). A generalizable sentiment analysis method for creating a hotel dictionary: Using big data on TripAdvisor hotel reviews. Journal of Hospitality and Tourism Technology, 12(4), 593–612. https://doi.org/10.1108/JHTT-02-2020-0034

Economic and Social Commission for Asia and the Pacific. (2021). Guidelines on the use of big data for official statistics. United Nations ESCAP. https://hdl.handle.net/20.500.12870/3723

Fuchs, M., Eberle, T., & Höpken, W. (2025). Google Maps data for tourism real-time monitoring and analytics: The case of cultural tourism, Sweden. Handbook on Big Data Marketing and Management in Tourism and Hospitality, 146–167. Edward Elgar Publishing. https://doi.org/10.4337/9781035300136.00014

Ipmawati, S., Pratiwi, D. D., & Nurhasanah, F. (2024). Sentiment analysis of tourist attractions based on reviews on Google Maps using the support vector machine algorithm. Malcom: Indonesian Journal of Machine Learning and Computer Science, 3(1), 31–39. https://doi.org/10.57152/malcom.v4i1.1066

Irvandi, I., Irawan, D., & Nurdiawan, F. (2023). Naïve Bayes and wordcloud for sentiment analysis of halal tourism in Lombok Island Indonesia. Innovatics, 5(1), 1–12. https://doi.org/10.37058/innovatics.v5i1.6675

Khalisa, T. N., & Ramadhani, D. P. (2025). Exploring tourist perceptions of Greater Bandung tourism destinations: An IndoBERT-based big data analytics study on Google Maps reviews. In 2025 International Conference on Data Science and Its Applications (pp. 658–663). IEEE. https://doi.org/doi: 10.1109/ICoCSETI63724.2025.11020554.

Krisbianto, O., Minantyo, H., Kristama, B. Y., & Oktaviana, C. (2023). Studi persepsi wisatawan terhadap produk makanan lokal ikonis Bromo: Implikasi bagi pengembangan industri pangan. Journal of Indonesian Tourism, Hospitality and Recreation, 6(2), 171–186. https://doi.org/10.17509/jithor.v6i2.57351

Kusuma, D. W., & Pratiwi, N. (2022). An interactive visualization of location-based reviews using word cloud and OpenStreetMap for tourism applications. Journal of Information Systems and Informatics, 4(3), 555–567. https://doi.org/10.33557/journalisi.v4i3.555

Li, Q., Li, S., Zhang, S., & Hu, J. (2019). A review of text corpus-based tourism big data mining. Applied Sciences, 9(16), 3300. https://doi.org/10.3390/app9163300

Liu, B. (2012). Sentiment analysis and opinion mining. Springer International Publishing. https://doi.org/10.1007/978-3-031-02145-9

Lyu, J., Khan, A., Bibi, S., Chan, J. H., & Qi, X. (2022). Big data in action: An overview of big data studies in tourism and hospitality literature. Journal of Hospitality and Tourism Technology, 13(2), 229–248. https://doi.org/10.1108/JHTT-09-2021-0243

Mariani, M., & Baggio, R. (2022). Big data and analytics in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management, 34(5), 1769–1797. https://doi.org/10.1108/IJCHM-03-2021-0301

Mathayomchan, B. (2019). Utilizing Google translated reviews from Google Maps in sentiment analysis for Phuket tourist attractions. In 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE. https://doi.org/10.1109/JCSSE.2019.8864150

Mellinas, J. P., & Martín-Fuentes, E. (2023). Tourism online reviews: Databases and samples. Handbook of e-Tourism, 1–19. Springer. https://doi.org/10.1007/978-3-030-05324-6_68-1

Pemerintah Kabupaten Belitung Timur. (2024). Rencana pembangunan jangka menengah daerah (RPJMD) Kabupaten Belitung Timur tahun 2025–2029. Bappelitbangda Kabupaten Belitung Timur.

Shin, B., Ryu, S., Kim, Y., & Kim, D. (2022). Analysis on review data of restaurants in Google Maps through text mining: Focusing on sentiment analysis. Journal of Multimedia Information System, 9(1), 61–72. https://doi.org/10.33851/JMIS.2022.9.1.61

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049

UN World Tourism Organization. (2021). COVID-19 and tourism: 2020 – A year in review. UNWTO. https://doi.org/10.18111/9789284422456

United Nations Statistics Division. (2020). Big data for official statistics: Handbook. United Nations. https://unstats.un.org/bigdata

Wang, L. (2024). Enhancing tourism management through big data: Design and review. Heliyon, 10(3). https://doi.org/10.1016/j.heliyon.2024.e28051

Wismaningtyas, T. A., Sinuraya, S. I., Nugraha, J. T., & Mahendradi, R. M. (2024). Desa Borobudur sebagai pendukung kawasan destinasi pariwisata super prioritas Candi Borobudur: Sebuah analisis komponen wisata. Journal of Indonesian Tourism, Hospitality and Recreation, 7(1), 69–82. https://doi.org/10.17509/jithor.v7i1.67817

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Published

2026-04-07

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Vol. 9 No. 1 (2026): April

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Articles

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