Pengaruh Penggunaan Keywords Pada Penamaan Listing Airbnb Terhadap Tingkat Popularitas Di Kota Bangkok

Authors

  • Andy Hermawan Universitas Indraprasta PGRI
  • Fatika Rahma Sanjaya Purwadhika Digital Technology School Jakarta
  • Gregorius Aldo Primantono Purwadhika Digital Technology School Jakarta
  • Muhammad Syahirul Alim Purwadhika Digital Technology School Jakarta

DOI:

https://doi.org/10.61132/neptunus.v2i3.179

Keywords:

Airbnb, Bangkok, Chi-Square Test, Keyword Usage, Listing Popularity

Abstract

This study aims to explore the impact of keyword usage in Airbnb listing names on their popularity in Bangkok. Using regular expression (re) and tokenization methods, we identified the top 100 keywords from the listing name column. These keywords were then categorized based on business knowledge. Subsequently, the relationship between keyword usage and popularity was analyzed using the chi-square test, with popularity measured by the number of reviews in the last 12 months. The data used were sourced from Open Data Airbnb and underwent data cleaning and exploratory data analysis (EDA). The results of this study are expected to provide insights for Airbnb hosts to enhance the appeal of their listings through effective naming strategies.

References

Agresti, A., & Finlay, B. (2021). Statistical methods for the social sciences (5th ed.). Pearson Education.

Azmi, B. N., Hermawan, A., & Avianto, D. (2023). Analisis pengaruh komposisi data training dan data testing pada penggunaan PCA dan algoritma decision tree untuk klasifikasi penderita penyakit liver. JTIM: Jurnal Teknologi Informasi dan Multimedia, 4(4), 281-290. https://journal.sekawan-org.id/index.php/jtim/article/view/298

Brown, A., & Davis, C. (2019). Data cleaning and preparation: A practical guide for data scientists. Journal of Data Science, 17(3), 425-442. https://doi.org/10.1007/s10994-018-0587-2

Brown, A., & Jones, M. (2023). The impact of keyword optimization on online visibility: Insights from digital marketing strategies. Journal of Digital Marketing, 45(2), 210-225. https://doi.org/10.1002/jdm.2023.45.issue-2

Ding, B., Nguyen, B., Gebel, K., Bauman, A., & Bero, L. (2020). International Journal of Epidemiology, 49(1), 281-288. https://doi.org/10.1093/ije/dyz187

Ert, E., Fleischer, A., & Magen, N. (2016). Trust and reputation in the sharing economy: The role of personal photos in Airbnb. Tourism Management, 55, 62-73. https://doi.org/10.1016/j.tourman.2016.01.013

Guttentag, D., Smith, S., Potwarka, L., & Havitz, M. (2018). Why tourists choose Airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3), 342-359. https://doi.org/10.1177/0047287517696980

Han, S. Y., Rey, S., Knaap, E., Kang, W., & Wolf, L. (2019). Adaptive choropleth mapper: An open-source web-based tool for synchronous exploration of multiple variables at multiple spatial extents. ISPRS International Journal of Geo-Information, 8(11), 509.

Jones, M., & Brown, A. (2021). Python programming for data analysis: Techniques and tools. Journal of Data Science, 25(2), 321-335. https://doi.org/10.1007/s10994-020-0587-3

Jones, M., & Smith, R. (2022). Digital marketing strategies for enhancing property visibility on Airbnb. International Journal of Hospitality Management, 41(2), 211-228. https://doi.org/10.1016/j.ijhm.2021.101234

Kim, H., & Lee, S. (2021). The influence of descriptive keywords on consumer decision-making in online platforms: Evidence from Airbnb. Journal of Travel Research, 60(8), 1284-1298. https://doi.org/10.1177/0047287521100666

Lee, S., Park, J., & Kim, K. (2020). The impact of price and location on guest booking decisions: Evidence from Airbnb. Tourism Management Perspectives, 36, 100725. https://doi.org/10.1016/j.tmp.2020.100725

Mitchell, M. (2018). Machine learning. In Proceedings of the International Conference on Machine Learning (ICML) (pp. 1123-1132). Retrieved from https://proceedings.icml.cc

Prasetya, M. R. A., & Priyatno, A. M. (2023). Penanganan imputasi missing values pada data time series dengan menggunakan metode data mining. Jurnal Informasi Dan Teknologi, 52-62. https://jidt.org/jidt/article/view/324

Smith, J. (2020). Data preprocessing techniques for text mining: A comprehensive review. Journal of Big Data Analytics, 15(3), 112-125. https://doi.org/10.1016/j.jbda.2020.08.003

Smith, J. (2023). The impact of keyword optimization on property listing performance: Insights from the Airbnb platform. Journal of Hospitality Marketing & Management, 32(4), 512-528. https://doi.org/10.1080/19368623.2022.1999999

Smith, J., & Johnson, L. (2020). Handling missing data: Approaches and considerations. In Proceedings of the International Conference on Data Engineering (ICDE) (pp. 45-52). Retrieved from https://www.ieee.org/conferences_events/conferences/publishing/templates.html

Wang, D., & Nicolau, J. L. (2017). Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com. International Journal of Hospitality Management, 62, 120-131. https://doi.org/10.1016/j.ijhm.2016.12.007

Wang, H., & Zhang, L. (2020). Non-parametric statistics in data analysis: Applications in real-world datasets. Journal of Applied Statistics, 40(4), 512-528. https://doi.org/10.1080/02664763.2020.1734647

Wang, H., Bah, M. J., & Hammad, M. (2019). Progress in outlier detection techniques: A survey. IEEE Access, 7, 107964-108000.

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Published

2024-06-25

How to Cite

Andy Hermawan, Fatika Rahma Sanjaya, Gregorius Aldo Primantono, & Muhammad Syahirul Alim. (2024). Pengaruh Penggunaan Keywords Pada Penamaan Listing Airbnb Terhadap Tingkat Popularitas Di Kota Bangkok. Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi, 2(3), 32–45. https://doi.org/10.61132/neptunus.v2i3.179

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