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Student Centric Model for Learning Analytics in Smart Campus Ecosystem: A Systematic Literature Review I Gusti Ngurah Suryantara; Jusia Amanda Ginting; Raphael Benedict Manuel
JURNAL SISFOTEK GLOBAL Vol 16, No 1 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16279

Abstract

The development of smart campuses has intensified the use of data-driven technologies to support institutional decision-making in higher education. However, many existing smart campus implementations remain system-oriented, with limited emphasis on learning processes and student needs. This study aims to formulate a student-centric model for learning analytics within digital twin–enabled smart campus ecosystems through a systematic literature review. The review follows the PRISMA 2020 guidelines and analyzes peer-reviewed articles indexed in the Scopus database, focusing on digital twins, smart campuses, learning analytics, and data governance. The findings indicate that digital twins have evolved from static digital representations into integrated platforms that combine real-time data, modeling, and analytics to support proactive decision-making. Nevertheless, the integration of learning analytics that explicitly centers on students is still fragmented. The concept of the student digital twin emerges as a promising approach for modeling learners as dynamic analytical entities, but it also raises critical concerns related to ethics, privacy, transparency, and governance. Based on the synthesis, this study proposes a conceptual student-centric model consisting of data sources, sensing mechanisms, student modeling, learning analytics, feedback and intervention pathways, and governance safeguards. The model provides a structured foundation for designing responsible and sustainable learning analytics in smart campus environments.
DIGITAL TWIN DRIVEN SMART CAMPUS DEVELOPMENT: CONCEPTS, CHALLENGES AND OPPORTUNITIES Jusia Amanda Ginting; I Gusti Ngurah Suryantara; Raphael Benedict Manuel
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.595

Abstract

This study aims to examine the development and implementation of digital twin technology in supporting smart campus ecosystems through a Systematic Literature Review approach. The study focuses on identifying technological trends, implementation opportunities, and various challenges that arise in the adoption of digital twin systems within higher education environments. The SLR method was conducted using the PRISMA framework to ensure a transparent and systematic article selection process. A total of 765 initial articles were identified from various academic databases with a publication range from 2015 to 2025. After undergoing the screening and selection process, 36 relevant articles were obtained for further analysis. The results show that digital twin technology has significant potential in supporting smart campus management through real-time monitoring of campus infrastructure, predictive maintenance of facilities, energy management optimization, and data-driven decision-making in university operations. In addition, this review also identifies several challenges in implementing digital twin systems, including data integration complexity, cybersecurity risks, high infrastructure investment requirements, and organizational readiness in facing digital transformation.
ANALISIS SENTIMEN DAN STRUKTUR SOSIAL DALAM PERDEBATAN DARING MENGENAI KEBIJAKAN MAKAN BERGIZI GRATIS (MBG) DI INDONESIA Jusia Amanda Ginting; I Gusti Ngurah Suryantara; Agustinus Fritz Wijaya; Teady Matius Surya Mulyana; Raphael Benedict Manuel
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.596

Abstract

Public policy discussions increasingly take place on social media, where public opinion is shaped not only by message content but also by patterns of user interaction. This study analyzes online conversations related to Indonesia’s Free Nutritious Meal (MBG) policy on platform X by integrating Social Network Analysis (SNA) and sentiment analysis. The dataset consists of 3,459 tweets collected between January 8 and February 11, 2025. Communication networks were constructed based on reply and mention relationships to identify interaction patterns and influential accounts. Sentiment analysis was conducted using a Natural Language Processing approach, with initial labeling based on BERT and further classification using a Support Vector Machine (SVM). Model performance was evaluated using accuracy, achieving a score of 91.78%. The findings reveal that MBG discussions form a relatively sparse yet highly centralized network dominated by a small number of accounts. Most tweets express neutral sentiment, while temporal analysis indicates a significant spike in activity on February 10, 2025. This study demonstrates that integrating network and sentiment analysis provides a more comprehensive understanding of how public opinion evolves in digital environments.