Star Hotel Occupancy Fluctuations in Makassar: Evidence from BPS Data and Implications for Islamic Tourism Marketing

Authors

  • Fajar Rakasiwi Syamsuddin Universitas Terbuka

DOI:

https://doi.org/10.37680/ijief.v6i1.9441

Keywords:

Hotel Occupancy Rate (TPK); Islamic Business Ethics; Tourism Seasonality; BPS Data; Marketing Strategy

Abstract

Occupancy rates of star hotels in Makassar fluctuate substantially; however, empirical patterns remain poorly documented due to the absence of systematic analysis of official data. This research gap necessitates a structured empirical investigation. This study empirically examined monthly occupancy fluctuations from March 2024 to June 2025 using secondary data from BPS (Central Bureau of Statistics). A qualitative document analysis was employed, involving month-to-month change calculations, peak and trough identification, and recovery pattern assessments. The findings revealed occupancy ranging from a peak of 59.32 percent in August 2024 to a trough of 32.30 percent in March 2025, representing a range of 27.02 percentage points. The market demonstrated rapid recovery, regaining over 70 percent of lost occupancy within one month. Notably, three months were missing from the data series. From an Islamic business ethics perspective, the sharp Ramadan trough requires fair treatment of workers and the avoidance of exploitative price hikes during peak periods such as August. Marketing strategies must uphold amanah (trustworthiness) by transparently reporting data gaps and maslahah (public interest) by ensuring promotional campaigns benefit local communities, not just hotel profits. The novelty of this study lies in integrating empirical occupancy fluctuation analysis with Islamic business ethics principles specifically justice, trustworthiness, and public interest into tourism marketing strategy, an approach absent in previous research. The study concludes that strengthening evidence-based marketing requires understanding peak, trough, and recovery patterns while improving data completeness and embedding Islamic ethical principles into every strategic decision.

References

Adanse, J., Khayiya, R., & Maranga, V. (2024). Influence of optimal room occupancy strategies on revenue maximisation during slack periods in selected 3-to 5-star hotels in Ghana. Research in Hospitality Management, 14(1), 57–69. https://doi.org/10.1080/22243534.2024.2357035

Binesh, F., Belarmino, A. M., van der Rest, J.-P., Singh, A. K., & Raab, C. (2024). Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model. International Journal of Contemporary Hospitality Management, 36(4), 1044–1065. https://doi.org/10.1108/IJCHM-10-2022-1233

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027

Busaina, L., Pramana, S., & Panuntun, S. B. (2023). Time-Series Clustering of the Regencies Hotel Room Occupancy Rate in Indonesia after the COVID-19 Pandemic. Proceedings of The International Conference on Data Science and Official Statistics, 2023(1), 344–353.

Butler, R. W. (2001). Seasonality in tourism: Issues and implications. In Seasonality in tourism (pp. 5–21). Routledge.

Chen, J., Li, C., Huang, L., & Zheng, W. (2025). Tourism demand forecasting: A deep learning model based on spatial-temporal transformer. Tourism Review, 80(3), 648–663. https://doi.org/10.1108/TR-05-2023-0275

Chen, J., Tong, K., Yu, Q., Chen, S., Balezentis, T., & Streimikiene, D. (2026). Innovative knowledge-based system for forecasting daily hotel operations amid external events using multi-source data: A time-varying parameter state-space model. Journal of Innovation & Knowledge, 11, 100858. https://doi.org/10.1016/j.jik.2025.100858

Contessi, D., Viverit, L., Pereira, L. N., & Heo, C. Y. (2024). Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis. International Journal of Hospitality Management, 121, 103802. https://doi.org/10.1016/j.ijhm.2024.103802

Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry. Theory Into Practice, 39(3), 124–130. https://doi.org/10.1207/s15430421tip3903_2

Hassan, A., & Pitanatri, P. D. S. (2025). Handbook of Tourism and Hospitality Marketing in Indonesia. Springer Nature.

Kotler, P., & Lane, K. K. (2016). Marketing Management.(15th global edition) Edinburgh. Pearson Education.(679 Pp). Kasem Bundit Journal, 18(2), 180–183.

Kumar, P., & Ekka, P. (2024). Statistical analysis of the impacts of COVID-19 pandemic on the small and large-scale tourism sectors in developing countries: P. Kumar et al. Environment, Development and Sustainability, 26(4). https://doi.org/10.1007/s10668-023-03112-4

Li, M., Zhang, C., Wang, S., & Sun, S. (2023). Multi-scale analysis-driven tourism forecasting: Insights from the peri-COVID-19. Current Issues in Tourism, 26(19), 3231–3254. https://doi.org/10.1080/13683500.2022.2144151

Li, N., Liu, X., & Meng, F. (2025). Machine Learning Techniques in Tourism and Hospitality Research: A Critical Assessment. Journal of Travel Research, 00472875251401912. https://journals.sagepub.com/doi/pdf/10.1177/00472875251401912

Liu, D., Zhao, W., & Luo, L. (2026). Restaurant daily demand prediction: modeling competition and agglomeration effects using spatial-temporal dual graph attention networks. Journal of Hospitality and Tourism Technology, 17(2), 501–518. https://doi.org/10.1108/JHTT-01-2024-0028

Mahalakshmi, S., & Bharath, H. (2025). From check-in to check-out: AI innovations in the Hospitality sector. In Digital disruption in hospitality, AI, and emerging technologies (pp. 79–99). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83608-796-020251005

Manoharan, G., & Ashtikar, S. P. (2025). AI era in hospitality: The new frontier in guest satisfaction and operations. https://doi.org/10.1108/978-1-83608-796-020251004

Marini, S., Fajri, I., & Karnita, I. (2025). Experience economy and digital marketing strategies in enhancing hotel competitiveness. Ekuitas (Jurnal Ekonomi Dan Keuangan), 9(3), 367–386.

Nhamo, G., Dube, K., & Chikodzi, D. (2020). Impacts and implications of COVID-19 on the global hotel industry and Airbnb. In Counting the Cost of COVID-19 on the global tourism industry (pp. 183–204). Springer.

Rahman, K. Y., Hanafi, H., & Sudarmi, S. (2022). Hotel Marketing Innovation Strategy in Tourism Recovery after Covid 19 Pandemic. Jurnal Ilmu Budaya, 10(2), 198–211.

Razak, M. (2022). Examining Linkage of Service Marketing Mix on Customer Satisfaction and Customer Loyalty: Hotel and Tourism Study. International Journal of Latest Research in Humanities and Social Science (IJLRHSS), 5(05), 162–181.

Rojas, C., & Jatowt, A. (2025). Transformer-based probabilistic forecasting of daily hotel demand using web search behavior. Knowledge-Based Systems, 310, 112966. https://doi.org/10.1016/j.knosys.2025.112966

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. john wiley & sons.

Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203–220.

Song, H., & Zhang, H. (2025). Tourism demand modelling and forecasting: a horizon 2050 paper. Tourism Review, 80(1), 8–27.

Badan statistik-indonesia-2023. (n.d.).

Sudiarta, M., & Ramadhanty, A. (2024). Marketing strategy in increasing occupancy rate at The Jayakarta Lombok Beach Resort & Spa. Bali Journal of Hospitality, Tourism and Culture Research, 2(1), 34–43.

Sugiyono. (2022). Metode Penelitian Kuantitatif. Alfabeta.

Sutari, N. W. A., Abd Kadir, H., & Rahman, A. (2020). Analysis of Occupancy Rates at Hotel Wisata Palu. International Journal of Health, Economics, and Social Sciences (IJHESS), 2(1), 1–5.

Tian, F., Yang, Y., Mao, Z., & Tang, W. (2021). Forecasting daily attraction demand using big data from search engines and social media. International Journal of Contemporary Hospitality Management, 33(6), 1950–1976. https://doi.org/10.1108/IJCHM-06-2020-0631

Velu, S. R., Ravi, V., & Tabianan, K. (2022). Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases. Health and Technology, 12(6), 1237–1258. https://link.springer.com/content/pdf/10.1007/s12553-022-00701-7.pdf

Wu, D. C., Zhong, S., Wu, J., & Song, H. (2025). Tourism and hospitality forecasting with big data: A systematic review of the literature. Journal of Hospitality & Tourism Research, 49(3), 615–634. https://journals.sagepub.com/doi/pdf/10.1177/10963480231223151

Wu, E. H. C., Hu, J., & Chen, R. (2022). Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries. Current Issues in Tourism, 25(3), 490–507. https://www.tandfonline.com/doi/abs/10.1080/13683500.2021.1989385

Wu, F., Zhang, Q., Law, R., & Zheng, T. (2020). Fluctuations in Hong Kong hotel industry room rates under the 2019 Novel Coronavirus (COVID-19) outbreak: Evidence from big data on OTA channels. Sustainability, 12(18), 7709. https://www.mdpi.com/2071-1050/12/18/7709

Zhang, D., & Wu, C. (2023). What online review features really matter? An explainable deep learning approach for hotel demand forecasting. Journal of the Association for Information Science and Technology, 74(9), 1100–1117. https://doi.org/10.1002/asi.24807

Zhang, H., & Lu, J. (2022). Forecasting hotel room demand amid COVID-19. Tourism Economics, 28(1), 200–221. https://doi.org/10.1177/13548166211035569

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Published

2026-05-26

How to Cite

Syamsuddin, F. R. (2026). Star Hotel Occupancy Fluctuations in Makassar: Evidence from BPS Data and Implications for Islamic Tourism Marketing. Indonesian Journal of Islamic Economics and Finance, 6(1), 69–90. https://doi.org/10.37680/ijief.v6i1.9441

Issue

Vol. 6 No. 1 (2026)

Section

Articles

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Copyright (c) 2026 Fajar Rakasiwi Syamsuddin

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