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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Teknologi
Dian Sri Purwanti
Universitas Teknokrat Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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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.
Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Co-Authors Amarudin Erliyan Redy Susanto Muhammad Fadli Muhammad Surono Rifka Simbolon Riyan Maruly Valdi Mughni Budiman Windia Hanifah