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pinaria, ni wayan chintia
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Analisis Sentimen Ulasan Tamu Untuk Meningkatkan Hunian Kamar Boutique Hotel di Kuta Candra, Dewa Ayu Kirana Maya; Pitanatri, Putu Diah Sastri; Pinaria, Ni Wayan Chintia
Studi Ilmu Manajemen dan Organisasi Vol. 6 No. 2 (2025): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i2.4701

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.
Analisis Sentimen dalam Mengurangi Pembatalan Reservasi di The Westin Resort & Spa Ubud Puspaningrum, Ni Kadek Indah; Pitanatri, Putu Diah Sastri; Pinaria, Ni Wayan Chintia
Studi Ilmu Manajemen dan Organisasi Vol. 6 No. 2 (2025): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/simo.v6i2.4707

Abstract

Purpose: This study aimed to analyze guest sentiment from reviews on Booking.com to identify insights that may help reduce room reservation cancellations. Methodology/approach: A quantitative approach was used, with data collected through web scraping using Python from customer reviews on the Booking. com website. A total of 433 reviews were analyzed using the Naïve Bayes classification method for sentiment analysis. Results/findings: The analysis revealed that 362 reviews (83.6%) contained positive sentiments, indicating high guest satisfaction, particularly with staff service, room quality, and facilities such as the pool and breakfast. Meanwhile, 71 reviews (16.4%) expressed negative sentiments, mainly focusing on room quality and overall hotel experience. The Naïve Bayes model achieved a classification accuracy of 91%, with a high F1-score of 95% for positive sentiments but only 31% for negative sentiments, highlighting data imbalance. Based on these findings, hotel management is advised to pay more attention to key aspects such as “staff,” “room,” “pool,” and “breakfast” to enhance guest satisfaction and minimize reservation cancellations. Conclusion: Most reviews reflected positive sentiments, indicating a high level of satisfaction. However, negative reviews, although fewer, must be further evaluated to improve service quality, especially given the classification model’s lower performance on negative sentiments. Limitations: This study is limited to Booking.com reviews for The Westin Resort & Spa Ubud, based on 433 entries. Contribution: This study provides a sentiment analysis approach to help hotel management better understand customer feedback and develop strategies to reduce cancellation rates.
Co-Authors Andy Nudyanto Anggani, Mayang Fitri Artajaya, Made Astuti Pranadewi, Putu Mira Candra, Dewa Ayu Kirana Maya Citrawati, Luh Putu Dewi, A.A. Ayu Indah Kusuma Febrianto, I Gusti Agung I Gusti Agung Febrianto I Gusti Ngurah Agung Suprastayasa I Gusti Ngurah Agung Wiryanata I Ketut Surata I Putu Gde Sukaatmadja I Wayan Adi Pratama Jata, I Wayan Juli Rastitiati, Ni Kade Kalpikawati, Ida Ayu Kusumarini, Indah Loanata, Cahyo Mayang Fitri Anggani Negara, I Dewa Agung Anom Segening Putra Ni Desak Made Santi Diwyarthi Ni Kadek Vira Kumara Dewi Ni Luh Putu Agustina Darmayanti Ni Made Suastini, Ni Made Pratiwi, Kadek Andita Dwi Puspaningrum, Ni Kadek Indah Putu Yudi Setiawan Rumadana, I Made Sabudi, I Nyoman Sukana Santi Diwyarthi, Ni Desak Made Sastri Pitanatri, Putu Diah Sri Sadjuni, Ni Luh Gede Sudiksa, I Nyoman Sulistyawati, Ni Luh Ketut Sri Swandewi, Ni Kadek Usadi, Made Pradnyan Permana Wiartha, Nyoman Gede Mas