The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach

https://doi.org/10.35912/joste.v7i1.2927
Published: Sep 8, 2025

Abstract:

Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies.

Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations.

Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively.

Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences.

Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests.

Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.

Keywords:
1. ARIMA
2. Guest Preference
3. Occupancy
4. Seasonality
5. Time-Series
Authors:
1 . Luh Made Gunapria Hindu Rajeswari Pamungkas
Bali Tourism Polytechnic, Bali, Indonesia
2 . Putu Diah Sastri Pitanatri
Bali Tourism Polytechnic, Bali, Indonesia
https://orcid.org/0000-0002-8892-7657
3 . Clearesta Adinda
Bali Tourism Polytechnic, Bali, Indonesia
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How to Cite
Pamungkas, L. M. G. H. R. ., Pitanatri, P. D. S. ., & Adinda, C. (2025). The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach. Journal of Sustainable Tourism and Entrepreneurship, 7(1), 107–123. https://doi.org/10.35912/joste.v7i1.2927
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Issue & Section
Vol. 7 No. 1 (2025): September
Articles
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References

    Apaza-Panca, C., Ramos, K., Ramos, A., Saico, C., & Apaza-Apaza, S. (2024). Quality Of Service Of Accommodation Establishments As A Factor In Tourism Competitiveness. International Journal of Innovative Research and Scientific Studies, 7(3), 1128-1139. doi:https://doi.org/10.53894/ijirss.v7i3.3100

    Athiyarath, S., Paul, M., & Krishnaswamy, S. (2020). A Comparative Study And Analysis Of Time Series Forecasting Techniques. SN Computer Science, 1(3), 175. doi:https://doi.org/10.1007/s42979-020-00180-5

    Ayuningtyas, I., & Wirawati, I. (2020). Nowcasting Tingkat Penghunian Kamar Hotel Menggunakan Google Trends. Seminar Nasional Official Statistics, 2020(1), 338-343. doi:https://doi.org/10.34123/semnasoffstat.v2020i1.636

    Baleiro, R. (2023). Understanding Visitors’ Experiences At Portuguese Literary Museums: An Analysis Of Tripadvisor Reviews. European Journal of Tourism Research, 33, 3305-3305. doi:https://doi.org/10.54055/ejtr.v33i.2839

    Bassett, K. (2024). TripAdvisor as a ‘geo-pastoral technology’. Tourism Geographies, 26(4), 672-686. doi:https://doi.org/10.1080/14616688.2023.2275734

    Bawana, T. A., Mansor, F., & Noordin, K. (2024). Gauging Customer Sentiment Regarding Indonesian Islamic Digital Banks. AL-IKTISAB: Journal of Islamic Economic Law, 8(1), 101-118. doi:https://doi.org/10.21111/aliktisab.v8i2.12838

    Corluka, G. (2019). Tourism seasonality–an overview. Journal of business paradigms, 4(1), 21-43.

    Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, And Mixed Methods Approaches: Sage publications.

    Doran, A., & Schofield, P. (2023). Tourism and the seasons. Tourism: A temporal analysis, 29-42. doi:https://doi.org/10.23912/9781911635840-5446

    Indradewi, I. G. A. A. D. M., Ni Putu Sri, & Parwita, W. G. S. (2022). Peramalan Tingkat Penghunian Kamar Berdasarkan Kelas Hotel Di Bali Menggunakan Metode ARIMA Forecasting Room Occupancy Rates Based on Hotel Class in Bali Using the ARIMA Method. Journal of Computing Engineering, System and Science, 7(2), 325-339. doi:https://doi.org/10.24114/cess.v7i2.33959

    Jain, P. K., Pamula, R., & Srivastava, G. (2021). A Systematic Literature Review On Machine Learning Applications For Consumer Sentiment Analysis Using Online Reviews. Computer science review, 41, 100413. doi:https://doi.org/10.1016/j.cosrev.2021.100413

    Jatmiko, H., & Sandy, S. R. O. (2020). Faktor–Faktor Yang Mempengaruhi Tingkat Hunian Kamar Pada Hotel Di Kota Jember. Sadar Wisata: Jurnal Pariwisata, 3(1), 32-40. doi:https://doi.org/10.32528/sw.v3i1.3371

    Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review Of Arima Vs. Machine Learning Approaches For Time Series Forecasting In Data Driven Networks. Future Internet, 15(8), 255. doi:https://doi.org/10.3390/fi15080255

    Kristiyanti, D. A., & Sumarno, Y. (2020). Penerapan Metode Multiplicative Decomposition (Seasonal) Untuk Peramalan Persediaan Barang Pada Pt. Agrinusa Jaya Santosa. Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan), 3(2), 45-51. doi:https://doi.org/10.47970/siskom-kb.v3i2.145

    Kuncoro, H., & Kusumawati, N. (2021). A Study Of Customer Preference, Customer Perceived Value, Sales Promotion, And Social Media Marketing Towards Purchase Decision Of Sleeping Product In Generation Z. Adv. Int. J. Business, Entrep. SMEs, 3(9), 265-276. doi:https://doi.org/10.35631/aijbes.39018

    Maulana, A., & Koesfardani, C. F. P. P. (2021). Pola Musiman Kunjungan Wisatawan Mancanegara Ke Bali. Jurnal Kepariwisataan Indonesia: Jurnal Penelitian Dan Pengembangan Kepariwisataan Indonesia, 14(2), 73-90. doi:https://doi.org/10.47608/jki.v14i22020.73-90

    Mitra, S. K. (2020). Estimating The Duration Of Different Seasons And Their Impact On Hotel Room Prices. International Journal of Hospitality Management, 90, 102604. doi:https://doi.org/10.1016/j.ijhm.2020.102604

    Murbarani, N. (2020). Pemodelan Tingkat Hunian Kamar (Occupancy) Hotel Grand Inna Malioboro Yogyakarta Berdasarkan Pendekatan Analisis Intervensi.

    Nongthombam, K., & Sharma, D. (2021). Data Analysis Using Python. Int. J. Eng. Res. Technol, 10(07), 463-468. doi:https://doi.org/10.17577/IJERTV10IS070241

    Pramudita, A. (2020). Memperkirakan Tingkat Penghuni Hotel Menggunakan Analisis Arima Dengan Aplikasi Minitab. EDUSAINTEK, 4.

    Pujiningrum, R. D., & Sulistijanti, W. (2024). Peramalan Tingkat Penghunian Kamar Hotel Bintang Lima Provinsi Bali Menggunakan Metode ARIMA dan Fuzzy Time Series Lee. JURNAL SOSIAL EKONOMI DAN HUMANIORA, 10(2), 158-166. doi:https://doi.org/10.29303/jseh.v10i2.520

    Putri, C. R., Prabudhi, S. A., Putranto, F. G. F., Astasia, A., & Tjahjo, T. W. (2024). Forecasting Room Occupancy Rates in Batu City: Implicationsfor Government Policy Using ARIMA Method. PANGRIPTA, 7(1), 37-48. doi:https://doi.org/10.58411/gzftfs54

    Raatikainen, M., Kettunen, E., Salonen, A., Komssi, M., Mikkonen, T., & Lehtonen, T. (2021). State Of The Practice In Application Programming Interfaces (Apis): A Case Study. European Conference on Software Architecture, 191-206. doi:https://doi.org/10.1007/978-3-030-86044-8_14

    Rizalde, F. A., Mulyani, S., & Bachtiar, N. (2021). Forecasting Hotel Occupancy Rate In Riau Province Using Arima And Arimax. Proceedings of The International Conference on Data Science and Official Statistics, 2021(1), 578-589. doi:https://doi.org/10.34123/icdsos.v2021i1.199

    Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research Methods for Business Students.

    Singgalen, Y. A. (2025). Tourist Preferences at Hotel and Resort Based on Review Data. Journal of Business and Economics Research (JBE), 6(1), 179-190. doi:https://doi.org/10.47065/jbe.v6i1.6844

    Sirisuriya, S. D. S. (2023). Importance Of Web Scraping As A Data Source For Machine Learning Algorithms-Review. 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 134-139. doi:https://doi.org/10.1109/iciis58898.2023.10253502

    Sorlury, F. N., Mongi, C. E., & Nainggolan, N. (2022). Penggunaan Model Autoregressive Integrated Moving Average (ARIMA) Untuk Meramalkan Nilai Tukar Petani (NTP) di Provinsi Sulawesi Utara. d'Cartesian: Jurnal Matematika dan Aplikasi, 11(1), 59-66. doi:https://doi.org/10.35799/dc.11.1.2022.38744

    Syahputri, A. Z., Della Fallenia, F., & Syafitri, R. (2023). Kerangka berfikir penelitian kuantitatif. TARBIYAH: Journal of Educational Science and Teaching, 2(1), 160-166. doi:https://doi.org/10.1342/tarbiyah.v2i1.25

    Teichert, T., González-Martel, C., Hernández, J. M., & Schweiggart, N. (2024). Dynamics In Accommodation Feature Preferences: Exploring The Use Of Time Series Analysis Of Online Reviews For Decomposing Temporal Effects. International Journal of Contemporary Hospitality Management, 36(7), 2521-2541. doi:https://doi.org/10.1108/ijchm-03-2023-0279

    Velea, L., Gallo, A., Bojariu, R., Irimescu, A., Craciunescu, V., & Puiu, S. (2022). Holiday Climate Index: Urban—Application For Urban And Rural Areas In Romania. Atmosphere, 13(9), 1519. doi:https://doi.org/10.3390/atmos13091519

    Yabanc?, O. (2023). Managing Seasonality In Tourism. lnternational Journal of Geography and Geography Education(50), 353-369. doi:https://doi.org/10.32003/igge.1299610

    Zvaigzne, A., Litavniece, L., & Dembovska, I. (2022). Tourism Seasonality: The Causes And Effects. Worldwide Hospitality and Tourism Themes, 14(5), 421-430. doi:https://doi.org/10.1108/whatt-07-2022-0080

  1. Apaza-Panca, C., Ramos, K., Ramos, A., Saico, C., & Apaza-Apaza, S. (2024). Quality Of Service Of Accommodation Establishments As A Factor In Tourism Competitiveness. International Journal of Innovative Research and Scientific Studies, 7(3), 1128-1139. doi:https://doi.org/10.53894/ijirss.v7i3.3100
  2. Athiyarath, S., Paul, M., & Krishnaswamy, S. (2020). A Comparative Study And Analysis Of Time Series Forecasting Techniques. SN Computer Science, 1(3), 175. doi:https://doi.org/10.1007/s42979-020-00180-5
  3. Ayuningtyas, I., & Wirawati, I. (2020). Nowcasting Tingkat Penghunian Kamar Hotel Menggunakan Google Trends. Seminar Nasional Official Statistics, 2020(1), 338-343. doi:https://doi.org/10.34123/semnasoffstat.v2020i1.636
  4. Baleiro, R. (2023). Understanding Visitors’ Experiences At Portuguese Literary Museums: An Analysis Of Tripadvisor Reviews. European Journal of Tourism Research, 33, 3305-3305. doi:https://doi.org/10.54055/ejtr.v33i.2839
  5. Bassett, K. (2024). TripAdvisor as a ‘geo-pastoral technology’. Tourism Geographies, 26(4), 672-686. doi:https://doi.org/10.1080/14616688.2023.2275734
  6. Bawana, T. A., Mansor, F., & Noordin, K. (2024). Gauging Customer Sentiment Regarding Indonesian Islamic Digital Banks. AL-IKTISAB: Journal of Islamic Economic Law, 8(1), 101-118. doi:https://doi.org/10.21111/aliktisab.v8i2.12838
  7. Corluka, G. (2019). Tourism seasonality–an overview. Journal of business paradigms, 4(1), 21-43.
  8. Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, And Mixed Methods Approaches: Sage publications.
  9. Doran, A., & Schofield, P. (2023). Tourism and the seasons. Tourism: A temporal analysis, 29-42. doi:https://doi.org/10.23912/9781911635840-5446
  10. Indradewi, I. G. A. A. D. M., Ni Putu Sri, & Parwita, W. G. S. (2022). Peramalan Tingkat Penghunian Kamar Berdasarkan Kelas Hotel Di Bali Menggunakan Metode ARIMA Forecasting Room Occupancy Rates Based on Hotel Class in Bali Using the ARIMA Method. Journal of Computing Engineering, System and Science, 7(2), 325-339. doi:https://doi.org/10.24114/cess.v7i2.33959
  11. Jain, P. K., Pamula, R., & Srivastava, G. (2021). A Systematic Literature Review On Machine Learning Applications For Consumer Sentiment Analysis Using Online Reviews. Computer science review, 41, 100413. doi:https://doi.org/10.1016/j.cosrev.2021.100413
  12. Jatmiko, H., & Sandy, S. R. O. (2020). Faktor–Faktor Yang Mempengaruhi Tingkat Hunian Kamar Pada Hotel Di Kota Jember. Sadar Wisata: Jurnal Pariwisata, 3(1), 32-40. doi:https://doi.org/10.32528/sw.v3i1.3371
  13. Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A Review Of Arima Vs. Machine Learning Approaches For Time Series Forecasting In Data Driven Networks. Future Internet, 15(8), 255. doi:https://doi.org/10.3390/fi15080255
  14. Kristiyanti, D. A., & Sumarno, Y. (2020). Penerapan Metode Multiplicative Decomposition (Seasonal) Untuk Peramalan Persediaan Barang Pada Pt. Agrinusa Jaya Santosa. Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan), 3(2), 45-51. doi:https://doi.org/10.47970/siskom-kb.v3i2.145
  15. Kuncoro, H., & Kusumawati, N. (2021). A Study Of Customer Preference, Customer Perceived Value, Sales Promotion, And Social Media Marketing Towards Purchase Decision Of Sleeping Product In Generation Z. Adv. Int. J. Business, Entrep. SMEs, 3(9), 265-276. doi:https://doi.org/10.35631/aijbes.39018
  16. Maulana, A., & Koesfardani, C. F. P. P. (2021). Pola Musiman Kunjungan Wisatawan Mancanegara Ke Bali. Jurnal Kepariwisataan Indonesia: Jurnal Penelitian Dan Pengembangan Kepariwisataan Indonesia, 14(2), 73-90. doi:https://doi.org/10.47608/jki.v14i22020.73-90
  17. Mitra, S. K. (2020). Estimating The Duration Of Different Seasons And Their Impact On Hotel Room Prices. International Journal of Hospitality Management, 90, 102604. doi:https://doi.org/10.1016/j.ijhm.2020.102604
  18. Murbarani, N. (2020). Pemodelan Tingkat Hunian Kamar (Occupancy) Hotel Grand Inna Malioboro Yogyakarta Berdasarkan Pendekatan Analisis Intervensi.
  19. Nongthombam, K., & Sharma, D. (2021). Data Analysis Using Python. Int. J. Eng. Res. Technol, 10(07), 463-468. doi:https://doi.org/10.17577/IJERTV10IS070241
  20. Pramudita, A. (2020). Memperkirakan Tingkat Penghuni Hotel Menggunakan Analisis Arima Dengan Aplikasi Minitab. EDUSAINTEK, 4.
  21. Pujiningrum, R. D., & Sulistijanti, W. (2024). Peramalan Tingkat Penghunian Kamar Hotel Bintang Lima Provinsi Bali Menggunakan Metode ARIMA dan Fuzzy Time Series Lee. JURNAL SOSIAL EKONOMI DAN HUMANIORA, 10(2), 158-166. doi:https://doi.org/10.29303/jseh.v10i2.520
  22. Putri, C. R., Prabudhi, S. A., Putranto, F. G. F., Astasia, A., & Tjahjo, T. W. (2024). Forecasting Room Occupancy Rates in Batu City: Implicationsfor Government Policy Using ARIMA Method. PANGRIPTA, 7(1), 37-48. doi:https://doi.org/10.58411/gzftfs54
  23. Raatikainen, M., Kettunen, E., Salonen, A., Komssi, M., Mikkonen, T., & Lehtonen, T. (2021). State Of The Practice In Application Programming Interfaces (Apis): A Case Study. European Conference on Software Architecture, 191-206. doi:https://doi.org/10.1007/978-3-030-86044-8_14
  24. Rizalde, F. A., Mulyani, S., & Bachtiar, N. (2021). Forecasting Hotel Occupancy Rate In Riau Province Using Arima And Arimax. Proceedings of The International Conference on Data Science and Official Statistics, 2021(1), 578-589. doi:https://doi.org/10.34123/icdsos.v2021i1.199
  25. Saunders, M. N. K., Lewis, P., & Thornhill, A. (2023). Research Methods for Business Students.
  26. Singgalen, Y. A. (2025). Tourist Preferences at Hotel and Resort Based on Review Data. Journal of Business and Economics Research (JBE), 6(1), 179-190. doi:https://doi.org/10.47065/jbe.v6i1.6844
  27. Sirisuriya, S. D. S. (2023). Importance Of Web Scraping As A Data Source For Machine Learning Algorithms-Review. 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), 134-139. doi:https://doi.org/10.1109/iciis58898.2023.10253502
  28. Sorlury, F. N., Mongi, C. E., & Nainggolan, N. (2022). Penggunaan Model Autoregressive Integrated Moving Average (ARIMA) Untuk Meramalkan Nilai Tukar Petani (NTP) di Provinsi Sulawesi Utara. d\'Cartesian: Jurnal Matematika dan Aplikasi, 11(1), 59-66. doi:https://doi.org/10.35799/dc.11.1.2022.38744
  29. Syahputri, A. Z., Della Fallenia, F., & Syafitri, R. (2023). Kerangka berfikir penelitian kuantitatif. TARBIYAH: Journal of Educational Science and Teaching, 2(1), 160-166. doi:https://doi.org/10.1342/tarbiyah.v2i1.25
  30. Teichert, T., González-Martel, C., Hernández, J. M., & Schweiggart, N. (2024). Dynamics In Accommodation Feature Preferences: Exploring The Use Of Time Series Analysis Of Online Reviews For Decomposing Temporal Effects. International Journal of Contemporary Hospitality Management, 36(7), 2521-2541. doi:https://doi.org/10.1108/ijchm-03-2023-0279
  31. Velea, L., Gallo, A., Bojariu, R., Irimescu, A., Craciunescu, V., & Puiu, S. (2022). Holiday Climate Index: Urban—Application For Urban And Rural Areas In Romania. Atmosphere, 13(9), 1519. doi:https://doi.org/10.3390/atmos13091519
  32. Yabanc?, O. (2023). Managing Seasonality In Tourism. lnternational Journal of Geography and Geography Education(50), 353-369. doi:https://doi.org/10.32003/igge.1299610
  33. Zvaigzne, A., Litavniece, L., & Dembovska, I. (2022). Tourism Seasonality: The Causes And Effects. Worldwide Hospitality and Tourism Themes, 14(5), 421-430. doi:https://doi.org/10.1108/whatt-07-2022-0080