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Workforce woman empowerment: Transforming challenges into opportunities in Karma Royal Bali as a timeshare hotel Priliani, Ni Luh Dita; Kartini, Luh Putu; Adyatma, Prastha; Pitanatri, Putu Diah Sastri; Pitanatri, Made Uttari
Bahasa Indonesia Vol 5 No 1 (2025): APRIL 2025
Publisher : School of Tourism, Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/jtce.v5i1.5167

This study identifies strategies to enhance women's empowerment in the workplace at Karma Royal Bali and examines how these efforts contribute to guest satisfaction within the timeshare hotel system. The research focuses on three properties: Karma Royal Candidasa, Karma Royal Jimbaran, and Karma Royal Sanur. This study used a qualitative methodology through in-depth interviews with women leaders and workers; the study explores themes of gender bias, work-life balance, and career development barriers. Findings highlight effective techniques these women implement to overcome challenges, including training programs, mentorship, and supportive workplace policies, ensuring high service quality and improving guest experiences. Therefore, this study implies that women's empowerment is vital in providing guest satisfaction.

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

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.

Eksplorasi Preferensi Wisatawan Domestik Menggunakan Analisis Sentimen pada Hotel Luxury di Bali Amarawati, Ni Putu Erika Dita; Pitanatri, Putu Diah Sastri; Pratiwi, Kadek Andita Dwi
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.v6i2.4702

Purpose: Competition in Bali’s hospitality industry is intensifying due to the growing number of star-rated hotels and available room capacity. This trend is driven by globalization, digital advancements, and changing consumer demands for service quality. This study aims to analyze domestic tourists’ preferences through sentiment analysis of online reviews to help optimize room occupancy rates. Methodology/approach: Using a text mining approach with the Naïve Bayes algorithm, this study analyzes 429 Tripadvisor reviews of The St. Regis Bali Resort. Data was collected via web scraping using Python, covering reviews from the past five years to reflect current guest preferences. Results/findings: The results show that 80.19% of the reviews express positive sentiment, indicating high satisfaction with service quality, staff professionalism, room comfort, and a strong brand image. The Naïve Bayes classifier achieved an accuracy of 83.72%, performing well in identifying positive sentiment, though less effective for neutral and negative reviews due to class imbalance. Conclusion: Sentiment analysis using Naïve Bayes effectively captures positive guest sentiment, though further refinement is needed for neutral and negative classifications. These insights support more precise service improvements and marketing strategies to boost loyalty and occupancy. Limitations: This study is limited to Tripadvisor reviews of a single luxury hotel in Bali, which may affect the generalizability of the findings. Contribution: The study highlights the strategic value of guest reviews in informing hotel decision-making, helping to tailor services and promotions to meet domestic tourists’ preferences more effectively.

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

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.

Analyzing TripAdvisor reviews to improve service quality at Courtyard Marriott Bali Nusa Dua Resort Vijaya, Bagaskara; Pitanatri, Putu Diah Sastri; Pratiwi, Kadek Andita Dwi
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2857

Purpose: This study aims to explore trends in guest satisfaction at the Courtyard by Marriott Bali Nusa Dua Resort by analyzing online reviews from TripAdvisor, with the objective of enhancing service quality. Methods: The research follows a systematic methodology that begins with scraping guest reviews from TripAdvisor, ensuring the collection of relevant data. Subsequently, a thorough data cleaning and preprocessing process is undertaken to guarantee high-quality data. The study then utilizes time series analysis, specifically the ARIMA model, to analyze the evolving patterns of guest satisfaction over time. Results/findings: The findings show that the majority of guest feedback is positive, indicating general satisfaction with the hotel. The ARIMA model reveals that guest satisfaction is highly influenced by previous satisfaction levels, suggesting a trend where past experiences strongly impact future perceptions. Conclusions: These results provide valuable insights into the key drivers of guest satisfaction, offering actionable recommendations for hotel management. By understanding the dynamic factors that influence guest experiences, management can improve service quality, respond more effectively to unexpected situations, and remain competitive in the market. Limitations: The ARIMA model does not account for external factors, such as holiday seasons or marketing changes, nor does it analyze the specific content of reviews or differentiate between guest segments. Additionally, comparisons with competitors can provide a broader strategic context for a more comprehensive understanding. Contribution: The combination of sentiment analysis and time series forecasting in this study offers a unique contribution, enabling data-driven decisions that support continuous service improvement and customer satisfaction.

The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach Pamungkas, Luh Made Gunapria Hindu Rajeswari; Pitanatri, Putu Diah Sastri; Adinda, Clearesta
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 1 (2025): September
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i1.2927

Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies. Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations. Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences. Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests. Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.

Analyzing TripAdvisor reviews to improve service quality at Courtyard Marriott Bali Nusa Dua Resort Vijaya, Bagaskara; Pitanatri, Putu Diah Sastri; Pratiwi, Kadek Andita Dwi
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2857

Purpose: This study aims to explore trends in guest satisfaction at the Courtyard by Marriott Bali Nusa Dua Resort by analyzing online reviews from TripAdvisor, with the objective of enhancing service quality. Methods: The research follows a systematic methodology that begins with scraping guest reviews from TripAdvisor, ensuring the collection of relevant data. Subsequently, a thorough data cleaning and preprocessing process is undertaken to guarantee high-quality data. The study then utilizes time series analysis, specifically the ARIMA model, to analyze the evolving patterns of guest satisfaction over time. Results/findings: The findings show that the majority of guest feedback is positive, indicating general satisfaction with the hotel. The ARIMA model reveals that guest satisfaction is highly influenced by previous satisfaction levels, suggesting a trend where past experiences strongly impact future perceptions. Conclusions: These results provide valuable insights into the key drivers of guest satisfaction, offering actionable recommendations for hotel management. By understanding the dynamic factors that influence guest experiences, management can improve service quality, respond more effectively to unexpected situations, and remain competitive in the market. Limitations: The ARIMA model does not account for external factors, such as holiday seasons or marketing changes, nor does it analyze the specific content of reviews or differentiate between guest segments. Additionally, comparisons with competitors can provide a broader strategic context for a more comprehensive understanding. Contribution: The combination of sentiment analysis and time series forecasting in this study offers a unique contribution, enabling data-driven decisions that support continuous service improvement and customer satisfaction.

Pengaruh Kualitas Website Dan Promosi Melalui Sosial Media Instagram Terhadap Minat Beli Wisatawan Domestik Di The Westin Resort & Spa Ubud, Bali Pande Putu, Dina Maharani; Pitanatri, Putu Diah Sastri; Clearesta Adinda
Journal of Hotel Management Vol. 2 No. 1 (2024): Journal of Hotel Management
Publisher : Program Studi Administrasi Perhotelan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52352/jhm.v2i1.1594

Purchase interest is a stage of tourist interest in a particular product or service that arisesfrom awareness and perception. Consumer buying interest will arise when consumers getinformation that can convince consumers about the products or services offered. Purchaseinterest is influenced by website quality and promotion. Promotion requires media indisseminating information. Promotional media that is easily accessible and can be tailored tothe needs of tourists is social media. The social media used is Instagram. This type of researchis quantitative, with the population being domestic tourists who have visited the website andInstagram of The Westin Resort & Spa Ubud, Bali in 2023. The data analysis techniques usedin this study are Multiple Linear Regression, t test, f test and Coefficient of Determination.

The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach Pamungkas, Luh Made Gunapria Hindu Rajeswari; Pitanatri, Putu Diah Sastri; Adinda, Clearesta
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 1 (2025): September
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i1.2927

Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies. Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations. Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences. Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests. Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.

Travel motivations, preferences, and characteristics of women solo travelers in Bali Pitanatri, Putu Diah Sastri; Adnyani, Ni Wayan Giri; Kartini, Luh Putu; Valeri, Marco
Journal of Applied Sciences in Travel and Hospitality Vol. 8 No. 1 (2025): JASTH: Journal of Applied Sciences in Travel and Hospitality
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/jasth.v8i1.63-78

This study explores solo women travelers in Bali, focusing on their travel motivations, preferences, and characteristics. The research aims to fill the gap in understanding how solo travel contributes to understanding women's travel in these popular destinations. The study focuses on Big Data analysis. Textual content from TripAdvisor reviews by solo woman travelers is analyzed using the BART Large Zero Shot model. This model classifies text according to Maslow's hierarchy of needs and Plog's psychographics model, identifying primary travel motivations and distinguishing between allocentric and psychocentric traveler types. The findings reveal that 36.39% of travelers are motivated by self-actualization, seeking personal growth and transformative experiences. Additionally, 83.79% of solo woman travelers prefer allocentric travel experiences, indicating a strong desire for adventure and cultural immersion. These results highlight the empowerment journey of solo woman travelers as they travel to new destinations independently. This research provides valuable insights into the empowerment of solo woman travelers, emphasizing the role of travel in fostering personal development and independence. By examining the specific motivations and preferences of these travelers, the study enriches the discourse on gender roles within modern tourism and offers a nuanced understanding of solo travel.

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