Analisis Sentimen Ulasan Tamu Untuk Meningkatkan Hunian Kamar Boutique Hotel di Kuta

https://doi.org/10.35912/simo.v6i2.4701
Published: Jul 22, 2025

Abstract:

Purpose: This study aims to analyze guest review sentiments of Fourteen Roses Boutique Hotel Kuta on the Booking.com platform, to identify aspects of hotel services that require improvement and can inform strategies to increase room occupancy rates.

Methodology/approach: A quantitative approach is used, with data collected via web scraping from Booking.com. The review texts undergo preprocessing for cleaning and structuring, followed by sentiment classification using the Naïve Bayes algorithm and TF-IDF for feature extraction. Python is used for analysis, and the results are visualized using word clouds and sentiment distribution charts.

Results/findings: The analysis reveals that 49.0% of the reviews express positive sentiment, highlighting appreciation for staff service, room comfort, facilities, and hotel location. Meanwhile, 21.0% show negative sentiment, mainly concerning breakfast quality, noise at night, and bathroom conditions. Additionally, 14.3% of the reviews are neutral, often using terms like “standard,” “ok,” or “normal,” indicating weakly held opinions. Despite data imbalance, the Naïve Bayes model achieved an accuracy of 78%.

Conclutions: Overall guest perceptions are positive, but negative and neutral feedback still requires attention. The sentiment analysis results and word cloud visualizations serve as useful references for identifying areas needing service improvements to enhance occupancy rates.

Limitations: The study focuses solely on Booking.com data. Future research should incorporate multiple platforms and explore more advanced classification techniques to better handle data imbalance.

Contribution: This study provides insights into guest sentiment that can help hotel management design more targeted strategies to remain competitive in the hospitality industry.

Keywords:
1. Booking.com
2. Guest Reviews
3. Naïve Bayes Sentiment analysis
4. Room Occupancy
Authors:
1 . Dewa Ayu Kirana Maya Candra
2 . Putu Diah Sastri Pitanatri
3 . Ni Wayan Chintia Pinaria
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How to Cite
Candra, D. A. K. M., Pitanatri, P. D. S., & Pinaria, N. W. C. (2025). Analisis Sentimen Ulasan Tamu Untuk Meningkatkan Hunian Kamar Boutique Hotel di Kuta. Studi Ilmu Manajemen Dan Organisasi, 6(2), 551–563. https://doi.org/10.35912/simo.v6i2.4701
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Issue & Section
Vol. 6 No. 2 (2025): Juli
Articles
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References

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    Humairah, H., Darmawan, I., & Pratiwi, O. N. (2020). Analisis Sentimen Ulasan Produk Toko Online Rubylicious Untuk Peningkatan Layanan Menggunakan Algoritma Naive Bayes. EProceedings of Engineering, 7(2).

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    Martin-Fuentes, E., & Mellinas, J. P. (2018). Hotels that most rely on Booking.com – online travel agencies (OTAs) and hotel distribution channels. Tourism Review, 73(4), 465–479. https://doi.org/10.1108/TR-12-2017-0201

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  1. Chyntia Morama, H., Ratnawati, D. E., & Arwani, I. (2022). Analisis Sentimen berbasis Aspek terhadap Ulasan Hotel Tentrem Yogyakarta menggunakan Algoritma Random Forest Classifier (Vol. 6, Issue 4). http://j-ptiik.ub.ac.id
  2. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168–177. https://doi.org/10.1145/1014052.1014073
  3. Humairah, H., Darmawan, I., & Pratiwi, O. N. (2020). Analisis Sentimen Ulasan Produk Toko Online Rubylicious Untuk Peningkatan Layanan Menggunakan Algoritma Naive Bayes. EProceedings of Engineering, 7(2).
  4. Jatmiko, H., & Sandy, S. R. O. (2020). Faktor–faktor yang mempengaruhi tingkat hunian kamar pada hotel di Kota Jember. Sadar Wisata: Jurnal Pariwisata, 3(1), 32–40.
  5. Jaya, I. K. A. A., Wiyasha, I. B. M., & Wardana, M. A. (2023). Pengaruh Kualitas Pelayanan dan Persepsi Harga Terhadap Kepuasan Tamu di The Trans Resort Bali. Jurnal Ilmiah Pariwisata Dan Bisnis, 2(2), 502–519.
  6. Kadir, P. A., & Modjo, M. L. (2021). Persepsi Tamu OTA Terhadap Dimensi Pelayanan Tangible, Resposiveness, Realibility, Empathy, Assurance di TC Damhil dalam Meningkatkan Tingkat Hunian Kamar. Ideas: Jurnal Pendidikan, Sosial, Dan Budaya, 7(4), 259–270.
  7. Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
  8. Marsiani, N. L. G., Putri, I., & Suarka, F. M. (2017). Pengaruh tingkat hunian kamar terhadap pendapatan makanan dan minuman di grand aston bali beach resort tanjung benoa bali. Jurnal Kepariwisataan Dan Hospitalitas, 1(2), 165–179.
  9. Martin-Fuentes, E., & Mellinas, J. P. (2018). Hotels that most rely on Booking.com – online travel agencies (OTAs) and hotel distribution channels. Tourism Review, 73(4), 465–479. https://doi.org/10.1108/TR-12-2017-0201
  10. Pohuda, N. (2023). THE INFLUENCE OF OTA CHANNELS ON THE TOURISM INDUSTRY. Scientific Notes of Taurida National V.I. Vernadsky University. Series: Economy and Management, 73(2). https://doi.org/10.32782/2523-4803/73-2-8
  11. Sihite, H. Y. (2024). PERAN FRONT OFFICE MANAGER DALAM MENGELOLA ONLINE REVIEW TAMU DI APLIKASI BOOKING. COM UNTUK MENINGKATKAN OCCUPANCY DI NATRA BINTAN, A TRIBUTE. JURNAL EKONOMI, SOSIAL & HUMANIORA, 6(02), 247–257.
  12. Thu, H. N. T., Minh, T. T., Ngoc, T. N. T., Nguyen, B. G., & Ngoc, L. N. (2021). Measuring Satisfaction and Loyalty of Guests Based on Vietnamese Hotel Online Reviews. International Journal of E-Entrepreneurship and Innovation, 11(2), 1–17. https://doi.org/10.4018/IJEEI.2021070101
  13. Yani, N. W. D. A., Marian, N. W. R., & Purnantara, I. M. H. (2023). Dampak E-Commerce dalam Meningkatkan Penjualan Kamar Hotel. Jurnal Ilmiah Pariwisata Dan Bisnis, 2(9), 2037–2046.
  14. Zhu, L., Lin, Y., & Cheng, M. (2020). Sentiment and guest satisfaction with peer-to-peer accommodation: When are online ratings more trustworthy? International Journal of Hospitality Management, 86, 102369. https://doi.org/10.1016/j.ijhm.2019.102369