Measuring Hotel Service Quality in Borobudur Temple Using Opinion Mining

A study analyzing hotel service quality at Borobudur Temple through opinion mining of online reviews reveals that the Support Vector Machine (SVM) algorithm outperformed others, achieving the highest accuracy, precision, and F1 score in classifying sentiment across key dimensions like Room, Facility, Surrounding, Employee, and Reliability. Notably, the 'Employee' dimension consistently showed the most positive sentiment among these. This research leverages HOLSERV Plus framework to assess customer perceptions, providing actionable insights for hotels while offering visitors a data-driven way to evaluate hospitality services in this UNESCO World Heritage Site.

Rus, A. M. M., Annisa, R., Surjandari, I., & Zulkarnain. (2019). Measuring Hotel Service Quality in Borobudur Temple Using Opinion Mining. 2019 16th International Conference on Service Systems and Service Management (ICSSSM), 1–5. https://doi.org/10.1109/icsssm.2019.8887650