Pengaruh Penggunaan Keywords Pada Penamaan Listing Airbnb Terhadap Tingkat Popularitas Di Kota Bangkok
DOI:
https://doi.org/10.61132/neptunus.v2i3.179Keywords:
Airbnb, Bangkok, Chi-Square Test, Keyword Usage, Listing PopularityAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.