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

Abstract

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 Ni Kadek Indah Puspaningrum; Putu Diah Sastri Pitanatri; Ni Wayan Chintia Pinaria
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.
ANALISIS SENTIMEN ULASAN ONLINE TRAVEL AGENT TERHADAP POTENSI MINAT BELI KONSUMEN DI LE MÉRIDIEN BALI I Kadek Pandu Adi Wiranatha; Putu Diah Sastri Pitanatri; Prastha Adyatma
Kepariwisataan: Jurnal Ilmiah Vol 20, No 2 (2026): Kepariwisataan: Jurnal Ilmiah
Publisher : Sekolah Tinggi Pariwisata Ambarrukmo Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47256/kji.v20i2.1163

Abstract

AbstractOnline customer reviews on Online Travel Agent (OTA) platforms serve as digital information sources that shape prospective consumers’ perceptions before hotel booking. This study aims to analyze the sentiment distribution of guest reviews for Le Méridien Bali Jimbaran on Booking.com and examine its potential implications for consumer purchase intention. A quantitative approach was employed through web scraping of 1,002 reviews from March 2023 to February 2026, after data screening and preprocessing, 538 valid reviews were analyzed. Sentiment analysis was conducted using VADER, TF-IDF feature extraction, and Multinomial Naïve Bayes classification. The model achieved an accuracy of 0.75, with an average precision of 0.73, recall of 0.59, and F1-score of 0.60. The results show that positive sentiment dominates at 67.84%, followed by neutral sentiment at 22.12% and negative sentiment at 10.04%. These findings indicate that guest perceptions are generally positive, although several service aspects still require improvement. Consumer purchase intention was not directly measured but discussed conceptually based on guest perceptions reflected in OTA reviews. This study provides managerial insights for strengthening digital reputation and supporting the potential formation of consumer purchase intention.Keywords: Sentiment Analysis; Online Travel Agent; Purchase Intention; Naïve Bayes; Booking.comAbstrakUlasan tamu pada platform Online Travel Agent (OTA) menjadi sumber informasi digital yang membentuk persepsi calon konsumen sebelum melakukan pemesanan hotel. Penelitian ini bertujuan untuk menganalisis distribusi sentimen ulasan tamu Le Méridien Bali Jimbaran pada Booking.com serta mengkaji potensi implikasinya terhadap minat beli konsumen. Penelitian ini menggunakan pendekatan kuantitatif melalui teknik web scraping terhadap 1.002 ulasan periode Maret 2023-Februari 2026, setelah seleksi dan pra-pemrosesan, diperoleh 538 ulasan valid untuk dianalisis. Analisis sentimen dilakukan menggunakan VADER, ekstraksi fitur TF-IDF, dan klasifikasi Multinomial Naïve Bayes. Model memperoleh akurasi 0,75, precision 0,73, recall 0,59, dan F1-score 0,60. Hasil menunjukkan dominasi sentimen positif sebesar 67,84%, diikuti sentimen netral 22,12% dan negatif 10,04%. Temuan ini menunjukkan bahwa persepsi tamu cenderung positif, meskipun masih terdapat aspek layanan yang perlu diperbaiki. Minat beli tidak diukur langsung, tetapi dibahas secara konseptual berdasarkan persepsi dalam ulasan OTA. Hasil penelitian memberikan masukan manajerial untuk memperkuat reputasi digital dan mendukung potensi pembentukan minat beli konsumen.Kata Kunci: Analisis Sentimen; Online Travel Agent; Minat Beli; Naïve Bayes; Booking.com
Evaluation on the Accuracy of Artificial Intelligence (AI) Assisted Revenue Management Forecasting: Evidence from a Luxury Resort in Bali Axell Pratama Kartika; Putu Diah Sastri Pitanatri; Ni Made Suastini
Indonesian Tourism Journal Vol. 3 No. 1 (2026): May, 2026
Publisher : CV. Austronesia Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69812/itj.v3i1.259

Abstract

This study examines the accuracy of artificial intelligence-assisted forecasting in supporting hotel revenue management at Amarterra Villas Resort Bali Nusa Dua, a five-star luxury resort operating within Bali’s premium leisure tourism market. The research was motivated by the growing need for hotels to improve forecasting reliability amid fluctuating demand, dynamic pricing conditions, and limited dedicated revenue management manpower. A descriptive quantitative research design with comparative forecasting accuracy analysis was applied using secondary operational data consisting of AI-generated forecasts and actual hotel performance records for Room Nights, Average Daily Rate (ADR), and Revenue. Forecasting accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), annual bias percentage, paired sample t-tests, and Pearson correlation analysis. The findings show that AI-assisted forecasting performed more accurately in predicting Room Nights than ADR and Revenue. Room Nights recorded relatively low forecast errors, with an MAE of 3.69, RMSE of 4.61, and MAPE of 11.65%, and the paired sample t-test indicated no statistically significant difference between forecasted and actual demand. In contrast, ADR and Revenue showed higher error values and statistically significant differences, indicating systematic underestimation in pricing and financial performance. Pearson correlation results further suggest that AI forecasts were useful in tracking general trends, although they remained less precise in predicting exact monetary outcomes.
The Effect of Green Hotel Implementation on Environmental Conservation at the Meru Sanur Felicia Made Sabina Maya Mahadewi; Putu Diah Sastri Pitanatri; Ida Ayu Kalpikawati
Jurnal Impresi Indonesia Vol. 5 No. 6 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i6.7844

Abstract

This study aims to analyze the influence of green hotel implementation on environmental conservation in The Meru Sanur through waste reduction and employee participation. The study uses an explanatory quantitative approach with a survey method of operational employees in the housekeeping, food & beverage, front office, and engineering departments. The data were analyzed using multiple linear regression. The results of the study show that the implementation of green hotels has a positive but not significant influence on environmental conservation. The waste reduction variable also showed a positive but not significant influence. Meanwhile, employee participation had the strongest relationship with environmental conservation, with a correlation value of 0.410 and a significance of 0.025 (<0.05). These findings show that the success of green hotel programs is more influenced by active employee involvement than by technical and environmental policy aspects alone. Therefore, increased awareness, training, and employee engagement are important factors in supporting environmental sustainability in the hospitality industry.
Strategi Pengemasan dan Pemasaran Produk Desa Wisata: Studi Kasus di Desa Tegaljadi Ni Desak Made Santi Diwyarthi; Putu Diah Sastri Pitanatri; Nyoman Gede Mas Wiartha; Ni Wayan Chintia Pinaria
ABDISOSHUM: Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdisoshum.v5i2.8505

Abstract

Community-based tourism encourages tourism villages to enhance the competitiveness of local products through more innovative packaging and marketing strategies. However, most entrepreneurs in Tegaljadi Village, Tabanan Regency, continue to face limitations in product packaging, brand identity development, and the utilization of digital media as promotional tools. This community service program aimed to improve entrepreneurs’ knowledge and understanding of product packaging and digital marketing to support sustainable tourism village development. The program was conducted from March to April 2026 and involved five MSME owners as a pilot project group. A participatory approach was employed through several stages, including observation, socialization, demonstrations, practical activities, and evaluation. The results indicate that participants’ average level of understanding increased from 2.80 to 4.19, representing an improvement of 49.64%. Furthermore, 80% of the participants began utilizing social media as a promotional tool, while 80% successfully developed stronger product identities through improved packaging designs and labeling. The program also fostered collaboration between MSME actors and the local community in promoting natural honey, traditional Balinese coffee, and jaje kaliadrem as flagship products of the tourism village. These findings suggest that community-based socialization can serve as an effective strategy for enhancing the capacity of local entrepreneurs while strengthening the long-term competitiveness of tourism villages.
Analyzing TripAdvisor reviews to improve service quality at Courtyard Marriott Bali Nusa Dua Resort Bagaskara Vijaya; Putu Diah Sastri Pitanatri; Kadek Andita Dwi Pratiwi
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

Abstract

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.
Sentiment Analysis of Airbnb Guest Reviews in Increasing Reservations at Tri Datu Properties Cokorda Zaic Arizena; Putu Diah Sastri Pitanatri; Ni Wayan Chintia Pinaria
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 4 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i4.4230

Abstract

Purpose: Few studies have applied sentiment analysis to identify service improvement priorities from Airbnb guest reviews. Therefore, this study aims to analyze guest review sentiments to generate insights for improving service quality and increasing reservations. Methodology: A quantitative computational approach was applied to 599 Airbnb guest reviews collected via API scraping. Reviews were processed in Python, transformed using term frequency-inverse TF-IDF, and classified using the Support Vector Machine (SVM) algorithm. A word cloud was used to visualize the key review topics. Results: The proposed SVM model achieved an accuracy of 98.33 %, demonstrating a robust sentiment classification performance. Most reviews (94.4%) expressed positive sentiments, highlighting property quality, host hospitality, and stay experience, whereas negative reviews (5.5%) primarily concerned cleanliness and facility maintenance. These findings identify key service attributes for improving guest satisfaction and supporting future reservations in the hospitality industry. Conclusions: Guest perceptions are largely positive, indicating high satisfaction, although negative reviews remain important to be addressed. These findings demonstrate that sentiment analysis can identify service improvement priorities to enhance service quality and increase reservation numbers. Limitations: This study focused on analyzing guest sentiment toward Tri Datu Properties on the Airbnb platform with 599 data entries. Contributions: This study extends the Expectation Confirmation Theory by integrating sentiment analysis and machine learning to transform large-scale Airbnb reviews into actionable insights for hospitality decision-making.
Peran Ulasan Tamu Online TripAdvisor pada Hotel Mewah: Pendekatan Analisis Sentimen untuk Meningkatkan Kinerja Hotel Sri Aida Melianty; Putu Diah Sastri Pitanatri; Ni Luh Ketut Sri Sulistyawati
Journal of Sustainable Tourism and Entrepreneurship Vol. 7 No. 4 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v7i4.4276

Abstract

Purpose: This study aims to analyze guest sentiment expressed in TripAdvisor reviews of Conrad Bali using the Multinomial Naïve Bayes algorithm to identify service attributes discussed by guests and generate evidence-based managerial insights that support hotel performance enhancement. Methodology: A quantitative text-mining approach was used. A total of 833 TripAdvisor reviews (2023–2025) were collected using the Apify web scraping tool.. The analysis involved text preprocessing, TF-IDF feature extraction, sentiment classification using the Multinomial Naïve Bayes algorithm, Synthetic Minority Oversampling Technique (SMOTE) for class balancing, confusion matrix evaluation, and word cloud visualization using Python. Results: The findings revealed that Positive, neutral, and negative sentiments accounted for 88.48%,8.64%and 2.88%, respectively. The application of SMOTE improved the model classification balance, increasing the F1-score from 0.8298 to 0.8596. Words related to staff, service, room, and hotel facilities appeared most frequently within positively classified reviews, indicating recurring discussion themes associated with favorable guest experiences. Conclusions: The findings demonstrate that sentiment analysis can effectively identify guest perceptions and recurring service attributes discussed in online hotel reviews. These insights support evidence-based service evaluation and managerial decision-making, thereby contributing to continuous service improvement and hotel performance enhancement. Limitations: The study was limited to TripAdvisor reviews, and the word clouds reflected word frequency rather than contextual meaning. In addition, the relatively small number of negative reviews may limit the classifier's ability to generalize minority sentiment patterns. Contributions: This study demonstrates how online guest reviews can be transformed into evidence-based managerial insights for service improvement, evaluation, and hotel performance enhancement.
Macau, Bali and the Malay World: A Gastronomic Perspective Annabel Jackson; Michael John Hitchcock; Putu Diah Sastri Pitanatri
Jurnal Kajian Bali (Journal of Bali Studies) Vol. 14 No. 2 (2024): Reclaiming Cultural Heritage
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JKB.2024.v14.i02.p05

Abstract

Macau’s location on the South China Sea suggests that any syncretic activity would have been of Sinitic-Portuguese variety. However, the situation is rather more nuanced, as the culture of the Macanese people, who consider themselves the ‘sons of the land’, reflects Portuguese colonial activity across the Indian Ocean and especially Malacca. Links between Macau and the ‘Malay World’ are deep and complex, and this paper approaches this issue through historical and political analysis, but also through the specifics of etymology and cuisine. The paper also draws comparisons with other ‘creolised’ cuisines in Southeast Asia such as Peranakan food in Malaysia and Western-Indonesian hybrids that have arisen in the tourism context of Bali. Given the importance played by women in preparing dishes for Portuguese men in Macau, parallels are drawn with the role of enslaved Hindu-Balinese women in colonial Batavia (Jakarta) who prepared pork dishes for their Dutch masters.
Co-Authors Ade Roy Renaldo Amarawati, Ni Putu Erika Dita Anak Agung Istri Ratna Sari Wulan Andreas Bryan Wagey Annabel Jackson Annabel Jackson Atmaja, I Putu Gede Brandon Wira Axell Pratama Kartika Ayu Trisna Sanjiwani Bagaskara Vijaya Cahyo Purnomo Loanata Candra, Dewa Ayu Kirana Maya Ciptasari, Dila Ayu Clearesta Adinda Cokorda Zaic Arizena Dewa Ayu Kirana Maya Candra Dewa Ketut Sujatha Dewa Ketut Sujatha Dewa Ketut Sujatha Duta, Putu Krisna Arya Dwilestari, Ni Kadek Felicia Made Sabina Maya Mahadewi I Dewa Gde Tri Jaya Cahyadi I Dewa Putu Hendri Pramana I Gede Made Sukariyanto I Gede Tito Yuda Pradana Jaya I Gusti Ayu Putu Diah Adnyani I Gusti Ayu Sucitadewi I Gusti Ngr Putu Jagadhita Rama Putra I Gusti Putu Ngurah Budiasa I Kadek Pandu Adi Wiranatha I Wayan Suteja Ida Ayu Kalpikawati Irene Hanna H Sihombing Kadek Andita Dwi Pratiwi Kristiana, Nina Indra Lindasari, Komang Leony Luh Putu Kartini Made Deny Kharisma Made Uttari Pitanatri Maharani, Putu Ayu Yoga Marco Valeri Maswiartha, Nyoman Gede Michael John Hitchcock Michael John Hitchcock Nathania, Ni Made Riska Ni Desak Made Santi Diwyarthi Ni Kade Juli Rastitiati Ni Kadek Indah Puspaningrum Ni Komang Thessy Aprelia Arijesni Ni Komang Thessy Aprelia Ni Komang Thessy Aprelia Ni Komang Thessy Aprelia, Ni Komang Thessy Aprelia Ni Made Suastini Ni Made Suastini Ni Made Suastini, Ni Made Ni Putu Erika Dita Amarawati Ni Putu Siska Putri Ni Wayan Chintia Pinaria Ni Wayan Giri Adnyani Nyoman Gede Mas Wiartha Pamungkas, Luh Made Gunapria Hindu Rajeswari Pande Putu, Dina Maharani pinaria, ni wayan chintia Pitanatri, Made Uttari Prabawathi, Ni Kadek Nia Prabhawati, Luh Gde Ayu Prastha Adyatma Priliani, Ni Luh Dita Puspaningrum, Ni Kadek Indah Putra, Gede Bagus Brahma Putu Bella Ayu Virdani Putu Bella Ayu Virdani Putu Devi Rosalina Putu Puja, Ida Bagus Rumadana, I Made Sabudi, I Nyoman Sukana Sri Aida Melianty Sugiarta, Komang Rikha Sujatha, Dewa Ketut Sulistyawati, Ni Luh Ketut Sri Suryananta, I Putu Bagus Vijaya, Bagaskara Virdani, Putu Bella Ayu Wagey, Andreas Bryan Wedari, Ni Kadek Maedha Wibawa, Anak Agung Putu Sebastian Ardika Wiguna, I Komang Bintang Tri Winata, Adi Sistha Yudhawijaya, Made