Time-Series Clustering of the Regencies Hotel Room Occupancy Rate in Indonesia after the COVID-19 Pandemic

Authors

  • Ladisa Busaina Politeknik Statistika STIS
  • Setia Pramana
  • Satria Bagus Panuntun

DOI:

https://doi.org/10.34123/icdsos.v2023i1.387

Keywords:

Dynamic Time Warping, web scraping, recovery

Abstract

After COVID-19 pandemic, Indonesia entering the recovery era. The government provides incentives for tourism industry recovery. This policy was created because the impact of COVID-19 pandemic on tourism industry at each regencies/cities are different. This study investigates a different recovery pattern at regencies/cities across Indonesia. The data of this study consist of the room occupancy rate (ROR) from Badan Pusat Statistik (BPS) Indonesia and from web scraping monthly data from Agoda website between 1 January 2021 until 1 August 2023. The regencies/cities are clustered by ROR category using the dynamic time warping method. The result of study, there is a difference of tourism industry recovery at regencies/cities across Indonesia, which is the speed are fast, medium, or slow. This could be the result of differences of different policy in each regency/city to respond COVID-19 pandemic on their tourism industry.

Downloads

Published

2023-12-29

How to Cite

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. https://doi.org/10.34123/icdsos.v2023i1.387