Abstract :

The development of internet users in Indonesia continues to increase over time, this makes customers comfortable with digital transactions. Trinusa Travelindo Company or better known as Traveloka, which is engaged in the online travel agent sector, has proven its extraordinary achievement, namely becoming the E-tourism application that is ranked 1st most visited by the Indonesian people in 2022. However, these results are also directly proportional to the number of negative reviews created in the application review itself. This study aims to identify what issues affect customer satisfaction with topic modelling. This study uses the text mining method derived from the results of Traveloka application user reviews. The data source obtained in this study is secondary data by using the Traveloka application review data crawling technique with samples from January to May. Based on sentiment analysis, positive sentiment is more dominant (50.38%) compared to negative sentiment (49.62%). User reviews are grouped based on the results of sentiment analysis, then Topic Modelling is carried out to find out what issues affect customer satisfaction. The topic modelling of the data used is Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) with the help of phyton 6.3. to find out what words and topics often appear or as factors that influence customer satisfaction. The most influential negative issues include the refund and payment process, pay later services, and the ordering experience which is considered complicated. On the other hand, issues that contribute positively to customer satisfaction include product quality, service quality, and promos and discounts. This is reinforced by the results of topic modelling which show that these aspects are often the main concerns in user reviews on the Google Play store. Thus, this study suggests that in order to increase customer satisfaction, Traveloka needs to focus on improving the quality of service, products, and offering more attractive promos, while simplifying the refund process and ordering experience in the application, some still say it is complicated

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Keywords :

Customer Satisfaction, Sentiment analysis, Text mining, Topic Modelling

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References :

1. Banchs, R. E. (2021). Text Mining with MATLAB®. Springer International Publishing. https://doi.org/10.1007/978-3-030-87695-1
2. Berners, P., & Martin, A. (2022). The Practical Guide to Achieving Customer Satisfaction in Events and Hotels. Routledge. https://doi.org/10.4324/9781003154600
3. Bhutada, S. (2020). TOPIC MODELING USING VARIATIONS ON LATENT DIRICHLET ALLOCATION. Ashok Yakkaldevi.
4. Biesok, G., & Wyród-Wróbel, J. (2011). Customer satisfaction-Meaning and methods of measuring. Marketing and Logistic Problems in the Management of Organization, 23–41.
5. Chung, J., Lee, J., & Yoon, J. (2022). Understanding music streaming services via text mining of online customer reviews. Electronic Commerce Research and Applications, 53, 101145. https://doi.org/10.1016/j.elerap.2022.101145
6. id. (2023). APJII: Pengguna Internet Indonesia 215,63 Juta pada 2022-2023. Https://Dataindonesia.Id/Digital/Detail/Apjii-Pengguna-Internet-Indonesia-21563-Juta-Pada-20222023.
7. Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161–168. https://doi.org/10.1016/j.jretconser.2018.02.006
8. Jo, T. (2019). Text Mining (Vol. 45). Springer International Publishing. https://doi.org/10.1007/978-3-319-91815-0
9. Kitsios, F., Kamariotou, M., Karanikolas, P., & Grigoroudis, E. (2021). Digital Marketing Platforms and Customer Satisfaction: Identifying eWOM Using Big Data and Text Mining. Applied Sciences, 11(17), 8032. https://doi.org/10.3390/app11178032
10. Kotler, P., & Keller, K. L. (2016). Marketing Management (15th ed.). Pearson Education.
11. Kotler, P., Keller, K. L., & Cherney, A. (2022). Marketing Management (16th ed.). Pearson Education.
12. Kumar, A., Chakraborty, S., & Bala, P. K. (2023). Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. Journal of Retailing and Consumer Services, 73, 103363. https://doi.org/10.1016/j.jretconser.2023.103363
13. Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A skill Building Approach (7th ed.). John Wiley &Sons Ltd.
14. Song, Y., Liu, K., Guo, L., Yang, Z., & Jin, M. (2022). Does hotel customer satisfaction change during the COVID-19? A perspective from online reviews. Journal of Hospitality and Tourism Management, 51, 132–138. https://doi.org/10.1016/j.jhtm.2022.02.027
15. Williady, A., Wardhani, H. N., & Kim, H.-S. (2022). A Study on Customer Satisfaction in Bali’s Luxury Resort Utilizing Big Data through Online Review. Administrative Sciences, 12(4), 137. https://doi.org/10.3390/admsci12040137
16. Zhang, N., Liu, R., Zhang, X.-Y., & Pang, Z.-L. (2021). The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining. Data Science and Management, 3, 22–32. https://doi.org/10.1016/j.dsm.2021.09.001
17. Tjiptono Fandy, Anastasia Diana (2020) Pemasaran : Penerbit Andi Yogyakarta
18. Berners, P., & Martin, A. (2022). The Practical Guide to Achieving Customer Satisfaction in Events and Hotels (1st ed.). Routledge. https://doi.org/10.4324/9781003154600
19. Moreno & Iglesias (2020) . Sentiment Analysis for Social Media Published: 22 November 2019.
20. Rajput, N., & Chauhan, S. (2019). Analysis of various sentiment analysis techniques. International Journal of Computer Science and Mobile Computing, 8(2), 75-79.
21. (2015). Metode Penelitian Manajemen Dan Bisnis Konvergensi Teknologi Komunikasi Dan Informasi. Bandung: Refika Aditama.