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All Journal Jurnal Krisnadana
Putu Eka Parianthana
Sistem Informasi, Universitas Bali Dwipa, Bali, Indonesia
Computer Science & IT Control & Systems Engineering

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Robust Forecasting Model of Hotel Room Occupancy Rates in Bali: A SARIMA Algorithm Approach Ayu Gde Chrisna Udayanie; I Wayan Adi Sparta; Putu Eka Parianthana
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.1102

Abstract

The dynamics of post-pandemic tourism recovery create significant fluctuations in Bali's hospitality industry, demanding a precise capacity management method. This study aims to build a robust forecasting model to project the hotel Room Occupancy Rate (TPK) using the Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm approach. This study utilizes monthly time series datasets sourced from the Central Bureau of Statistics (BPS) during the recovery phase, namely the period January 2022 to October 2025. The research methodology applies an analytical framework that includes stationarity test, differencing process, and identification of the optimal model through Auto-ARIMA mechanism to capture complex seasonal patterns. Model performance is validated using the statistical metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results show that the SARIMA(0,1,0)(1,0,0)[12] model architecture is the best model with the lowest Akaike Information Criterion (AIC) value. The performance evaluation resulted in a MAPE value of 8.68%, which indicates a "very good" level of accuracy in minimizing prediction errors. Based on the model, projections for the period November 2025 to October 2026 show a positive stability trend with an estimated average occupancy of 63.32% (range 58.01%-66.48%). This research contributes to providing tourism stakeholders with a reliable quantitative instrument for the formulation of pricing strategies and more efficient resource allocation.
Machine Learning-Based Sentiment Analysis of User-Generated Content: Insights from Google Maps Reviews Ayu Gde Chrisna Udayanie; I Wayan Adi Sparta; Putu Eka Parianthana; Ni Putu Dea Sillviari
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/cp9nwn24

Abstract

The rapid growth of user-generated content on digital platforms has provided valuable opportunities for understanding customer perceptions through sentiment analysis. Google Maps, as one of the most widely used location-based services, allows visitors to share their experiences and opinions regarding public facilities and commercial destinations. This study aims to analyze visitor sentiment toward Living World Bali using machine learning techniques. A total of 4,060 Google Maps reviews were collected and processed through several text preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The Term Frequency–Inverse Document Frequency (TF-IDF) method was employed for feature extraction, while the Random Forest algorithm was utilized for sentiment classification. Experimental results indicate that the proposed model achieved an accuracy of 91.75%, precision of 92.39%, recall of 98.84%, and an F1-score of 95.55%. The sentiment distribution analysis revealed that positive sentiment dominated the dataset, accounting for 84.70% of all reviews, suggesting a high level of customer satisfaction with Living World Bali. Furthermore, the findings demonstrate that the integration of TF-IDF and Random Forest provides an effective approach for classifying textual reviews in the tourism and retail domains. The proposed framework offers valuable insights for shopping center management in evaluating customer experiences and supporting data-driven decision-making processes to improve service quality and visitor satisfaction.
Co-Authors Ayu Gde Chrisna Udayanie Ayu Gde Chrisna Udayanie I Wayan Adi Sparta Ni Putu Dea Sillviari