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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Rifka Simbolon
Politeknik Negeri Lampung
Computer Science & IT Engineering

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PERANCANGAN SISTEM INFORMASI FRONT OFFICE HOTEL BERBASIS WEB DENGAN PEMODELAN UML DAN INTEGRASI SMART ROOM Muhammad Fadli; Rifka Simbolon; Izza Maulida Santoso; Valdi Mughni Budiman; Hadori Rosadi; Budi Rahman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7459

Abstract

The rapid advancement of digital technology has significantly transformed the hospitality industry, particularly in enhancing front office services as the primary point of interaction between guests and the hotel. This study aims to design a web-based front office information system integrated with smart room technology, utilizing Unified Modeling Language (UML) as the modeling approach. The developed system, named SIFOHSMART, combines administrative data management functions with intelligent service features based on the Internet of Things (IoT). The main innovation of this system lies in the automatic notification feature from smart panels in guest rooms to the front office, enabling real-time detection and handling of complaints. This research adopts a prototyping methodology, including literature review, field observations, stakeholder interviews, user requirements analysis, UML diagram design (use case and activity), and system testing. The results demonstrate that the system improves operational efficiency, accelerates service response time, and supports data-driven decision-making. The contribution of this study extends beyond technological innovation by incorporating a human-centered design approach that considers usability and convenience for hotel staff. This system has the potential to serve as a reference model for developing adaptive and sustainable hotel information systems in the modern hospitality sector.
PERSPEKTIF BIBLIOMETRIK TERHADAP INTEGRASI METAVERSE DALAM PENDIDIKAN: TREN PUBLIKASI, PENULIS, DAN ARAH PENELITIAN TERKINI Muhammad Fadli; Dian Sri Purwanti; Windia Hanifah; Valdi Mughni Budiman; Rifka Simbolon; Riyan Maruly; Amarudin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8047

Abstract

The Metaverse has emerged as a promising digital technology in education due to its ability to create immersive, interactive, and collaborative learning environments. This study aims to analyze the development of research on the integration of the Metaverse in education through a bibliometric approach. The research data were collected from the Dimensions database using the keywords “Metaverse” and “Education,” resulting in a total of 636 publications published between 2015 and 2025. Bibliometric analysis was conducted and visualized using VOSviewer software to identify publication trends, productive authors, leading contributing countries, and emerging research themes. The findings reveal a significant increase in the number of publications since 2021, with the highest publication output recorded in 2024. China, South Korea, and the United States were identified as the leading contributors to the field, while Hwang G.J. was recognized as one of the most prolific authors. Keyword network analysis indicates that the dominant research themes focus on the Metaverse, Virtual Reality, Augmented Reality, learning, and immersive learning experiences. Furthermore, emerging topics such as digital twins, artificial intelligence, and technology acceptance have begun to gain attention as potential directions for future research. These findings suggest that Metaverse research in education is developing in a multidisciplinary manner and holds significant potential to support the transformation of digital learning in the future.
ANALISIS SENTIMEN ULASAN HOTEL TRIPADVISOR MENGGUNAKAN TF-IDF DAN MACHINE LEARNING: PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE DAN LOGISTIC REGRESSION Muhammad Fadli; Yunita Fitri Yanti; Tina Nurzachra Latifah Rizki Liana; Rifka Simbolon; Ratu Sinar Sari Tanjung; Hadori Rosadi; Budi Rahman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8261

Abstract

Online reviews on platforms like TripAdvisor play a crucial role in consumer decision making and hotel reputation management. However, the massive and unstructured volume of data requires a fast and accurate automated analysis method. This study aims to analyze the sentiment of TripAdvisor hotel reviews and compare the performance of the Support Vector Machine (SVM) and Logistic Regression (LR) algorithms. The dataset consists of 20,491 reviews classified into three sentiment classes: positive, negative, and neutral. Feature extraction was performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method. To handle the class imbalance issue and ensure robust evaluation, the models were evaluated using Stratified 10-Fold Cross-Validation. The results indicate that Logistic Regression outperforms Support Vector Machine, achieving an average accuracy of 81.46% and an F1-Score of 83.02%, compared to Support Vector Machine which obtained an accuracy of 80.41% and an F1-Score of 82.20%. Both models showed excellent performance on the positive class but faced challenges on the neutral class due to semantic ambiguity and majority class dominance. In conclusion, Logistic Regression proves to be a more optimal, stable, and efficient model for sentiment classification on imbalanced data, making it highly applicable for assisting hotel management in monitoring customer feedback in real time.
Co-Authors Amarudin Budi Rahman Dian Sri Purwanti Hadori Rosadi Izza Maulida Santoso Muhammad Fadli Ratu Sinar Sari Tanjung Riyan Maruly Tina Nurzachra Latifah Rizki Liana Valdi Mughni Budiman Windia Hanifah Yunita Fitri Yanti