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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Eka Rahmawati
Universitas Bina Sarana Informatika, Jakarta, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Develompent of Machine Learning Model to Predict Hotel Room Reservation Cancellations Eka Rahmawati; Galih Setiawan Nurohim; Candra Agustina; Denny Irawan; Zainal Muttaqin
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.431

Abstract

The frequent cancellations of hotel room reservations have become a pressing issue for the hospitality industry, especially in high-tourism areas such as Borobudur, Indonesia. This research develops a predictive machine learning (ML) model to identify cancellation probabilities to support proactive decision-making for hotel management. Using datasets from Borobudur-based hotels, key variables such as booking lead time, arrival month, and reservation outcomes were analyzed. Random Forest demonstrated the best performance, achieving an accuracy of 86.36% with a precision of 88.06%, recall of 93.65%, and F1-score of 90.77%. Logistic Regression demonstrated moderate effectiveness, while Bayesian Networks underperformed, highlighting the importance of robust algorithms for such tasks. The findings underscore the potential of ML models, particularly Random Forest, to reduce financial losses and enhance operational efficiency in the hospitality sector by anticipating cancellations and facilitating better resource allocation
Clustering-based Machine Learning Approach For Predicting Tourism Trends From Social Media Behavior Candra Agustina; Eka Rahmawati
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.443

Abstract

Digital technology has significantly transformed tourist behavior, particularly in searching for, selecting, and sharing travel experiences. Social media has become a primary source of information, influencing travel decisions through real-time recommendations and user-generated content. However, the large volume of data generated by social media presents challenges in understanding and predicting tourist behavior. This study aims to analyze tourist behavior patterns using a clustering-based machine learning approach, specifically K-Means Clustering. The research examines engagement levels on platforms such as Instagram, TikTok, and TripAdvisor to categorize tourists into three key segments: Digital-Savvy Travelers, Passive Travelers, and Conservative Travelers. The results indicate that machine learning effectively analyzes large-scale tourism data, providing valuable insights for destination marketing, personalized recommendations, and service optimization. The findings highlight the potential of machine learning to identify emerging trends, improve customer segmentation, and enhance targeted promotional strategies. Understanding these patterns enables tourism businesses to create data-driven strategies aligned with modern travel behaviors. In a broader perspective, artificial intelligence can revolutionize tourism marketing, increase customer engagement, and improve the overall travel experience
Co-Authors Candra Agustina Denny Irawan Galih Setiawan Nurohim Zainal Muttaqin