cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andhika Rafi Hananto
Contact Email
andhikarh90@gmail.com
Phone
+62895422720524
Journal Mail Official
support@ijrm.net
Editorial Address
Puri Mersi Baru, Blok A2, Jl. Martadireja 2 Purwokerto, Kab. Banyumas,Jawa Tengah.
Location
Kab. banyumas,
Jawa tengah
INDONESIA
International Journal Research on Metaverse
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30626927     DOI : https://doi.org/10.47738/ijrm
Core Subject : Science,
Computer Science & IT
Virtual and augmented reality technologies Network infrastructure and architecture for the metaverse Digital economy and transactions in the metaverse Social and cultural aspects of virtual environments Development and design of content in the metaverse Impact of the metaverse on industries such as education, healthcare, entertainment, and business Regulation, policy, and ethics in the metaverse IJRM aims to foster interdisciplinary dialogue and collaboration, contributing to the body of knowledge that drives the adoption and evolution of metaverse technologies. Papers published in IJRM are grounded in rigorous research methods and are expected to articulate their implications for theory and practice clearly. Authors are encouraged to state their contributions to the state-of-the-art in the field explicitly. Subject Area and Category: The International Journal Research on Metaverse focuses on virtual and augmented reality, network infrastructure, digital economy, social and cultural impacts, content development, industry-specific applications, regulation and ethics, and practical case studies.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2026): Regular Issue March 2026" : 6 Documents clear
How Do Service Quality and Exhibition Experience Impact Revisit Intention: Evidence from Metaverse Exhibitions Zhou, Jinquan; Hong-Wai, Ho; Wang, Zhuoxiang
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.36

Abstract

This study investigates the impact of metaverse exhibitions on consumer behavior, focusing particularly on revisit intention. Using a quantitative research design, data were collected from 324 participants who attended exhibitions in Macau and Hengqin. The results obtained through Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that metaverse exhibitions significantly enhance revisit intention (β = 0.171, p = 0.007), perceived service quality (β = 0.237, p < 0.001), and exhibition experience (β = 0.284, p < 0.001). Both perceived service quality (β = 0.210, p < 0.001) and exhibition experience (β = 0.243, p < 0.001) positively influence revisit intention and act as significant mediators in these relationships. The total effect of the metaverse exhibition on revisit intention was β = 0.304 (p < 0.001), confirming strong direct and indirect effects. These findings underscore the critical role of service quality and experiential engagement in shaping visitor loyalty within virtual environments. Practically, the study suggests that exhibition organizers should leverage VR/AR technologies and interactive design strategies to enhance immersion and foster repeat visitation. Overall, this research highlights the transformative potential of the metaverse in redefining audience interaction and loyalty in virtual exhibition platforms.
Clustering Digital Governance Adoption Patterns in the Metaverse Using K-Means and DBSCAN Algorithms Widjaja, Andree Emmanuel; Hery; Toer, Guevara Ananta
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.42

Abstract

The rapid advancement of immersive digital environments has accelerated global interest in leveraging metaverse technologies as extensions of public governance systems. This study analyses citizen readiness and perception toward metaverse-based digital governance in The Gambia using two unsupervised machine learning algorithms: K-Means and DBSCAN, applied to a dataset of 115 survey responses. After preprocessing and feature standardization, the K-Means algorithm identified two distinct adoption clusters, consisting of Cluster 0 with 76 respondents and Cluster 1 with 39 respondents. The centroid projections in PCA space revealed a clear behavioural separation, with Cluster 1 exhibiting a substantially higher mean PC1 score (2.5270) compared to Cluster 0 (−1.2968), indicating stronger readiness, optimism, and trust among respondents in the former group. In contrast, DBSCAN produced a single dominant cluster of 107 respondents and identified 8 outliers, suggesting a generally cohesive perception landscape with a small number of respondents expressing atypical attitudes toward metaverse-enabled governance. Collectively, these findings demonstrate that while public sentiment toward metaverse governance is broadly aligned, significant intra-group differences exist, making behavioural segmentation crucial for informing policy strategies. The results underscore the need for tailored approaches that address both enthusiastic adopters and more cautious individuals to support equitable and inclusive metaverse governance adoption.
Sentiment and Concern Classification on Metaverse Governance Responses Using Naïve Bayes and Support Vector Machine (SVM) Ben-Othman, Jalel; Hariguna, Taqwa
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.43

Abstract

The rapid advancement of immersive technologies such as the metaverse has introduced new opportunities and challenges for digital governance. Understanding public perception of these technologies is essential for designing governance systems that are transparent, inclusive, and responsive to citizens’ needs. This study analyses public sentiment and concerns regarding the use of metaverse technology in governance by applying two machine learning algorithms: Naïve Bayes and SVM. The dataset, consisting of open-ended survey responses from participants in The Gambia, was pre-processed through tokenization, stopword removal, and TF-IDF vectorization before model implementation. The results indicate that both algorithms can classify sentiment into positive, neutral, and negative categories; however, SVM consistently outperforms Naïve Bayes across all evaluation metrics. The SVM model achieved an accuracy of 88.6 percent and an F1-score of 0.873, demonstrating superior capability in recognizing contextual and semantic nuances within short text responses. In contrast, Naïve Bayes tended to overclassify responses as neutral, reflecting its limitation in capturing word dependencies. These findings confirm that SVM is better suited for sentiment analysis involving complex linguistic expressions and context-dependent opinions. The study contributes to the growing body of research on artificial intelligence in public policy by demonstrating how machine learning can provide deeper insights into citizen perspectives on emerging digital technologies. Such analytical approaches can assist policymakers in identifying public expectations, addressing concerns, and fostering trust in metaverse-based governance systems.
Automated Identification of Gait Anomalies Using Deep Autoencoder and Isolation Forest for Hybrid Anomaly Detection Kim, Sangbum; Sangsawang, Thosporn
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.44

Abstract

Human gait analysis plays a vital role in assessing locomotor function, postural stability, and early detection of motor impairments. This study proposes an unsupervised hybrid anomaly detection framework that integrates PCA and Isolation Forest (IF) to automatically identify abnormal gait patterns using a Multivariate Biomechanical Dataset (MGAD) containing 5,000 gait samples. PCA was utilized to reduce dimensionality and compress correlated gait features while retaining 95.1% of the total variance, thereby preserving essential biomechanical information. The reconstruction errors obtained from PCA were subsequently analyzed using Isolation Forest to isolate anomalous gait instances. Experimental results demonstrate that the hybrid PCA–IF model effectively differentiates between normal and abnormal gait behaviors, achieving an ROC-AUC of 0.912 and an F1-score of 0.866, indicating strong discriminative capability and model stability. Feature-level reconstruction analysis revealed that stance phase duration, step length, and stride length are the most influential determinants of gait irregularities, aligning with established clinical findings in gait biomechanics. The proposed framework is computationally efficient, interpretable, and fully unsupervised, making it suitable for real-time clinical assessment, rehabilitation monitoring, and wearable healthcare applications. These findings highlight the potential of hybrid statistical–machine learning models in advancing automated gait diagnostics and intelligent mobility analytics.
Anomaly Detection in Blockchain-Based Metaverse Transactions Using Hybrid Autoencoder and Isolation Forest Models for Risk Identification and Behavioral Pattern Analysis El Emary, Ibrahiem M. M.; Brzozowska, Anna; Popławski, Łukasz; Dziekański, Paweł; Glova, Jozef
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.45

Abstract

The increasing complexity of transactions within blockchain-based metaverse ecosystems has intensified the need for robust anomaly detection systems capable of identifying fraudulent, automated, or irregular behaviors. This study proposes a Hybrid Autoencoder–Isolation Forest (AE–IF) model for detecting anomalies in metaverse blockchain transactions through a combination of deep feature reconstruction and ensemble-based isolation. The proposed framework leverages the Autoencoder’s ability to learn nonlinear feature representations and the Isolation Forest’s capacity to isolate sparse anomalies, enabling the detection of both global and local irregularities. Experimental evaluation using real-world transaction data demonstrates that the hybrid model outperforms individual methods, achieving a ROC-AUC of 0.952, Precision of 0.88, Recall of 0.86, and F1-Score of 0.87. The ROC and Precision–Recall analyses confirm the model’s superior discriminative power and stability across imbalanced data distributions. Furthermore, behavioral analysis reveals distinct high-risk transaction patterns, including extended user sessions, cross-regional fund transfers, and irregular purchase behaviors. The results highlight the hybrid model’s effectiveness not only in anomaly detection but also in uncovering underlying behavioral and geographical risk factors. The proposed framework provides a scalable foundation for intelligent financial risk monitoring and cyber-fraud detection in decentralized metaverse economies.
Hybrid Ensemble Learning for Anomaly Detection in Metaverse Transactions Using Isolation Forest, Autoencoder, and XGBoost Prakash, S.; Mary, S. Aruna; Sudhagar, G.; Batumalay, Malathy
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.46

Abstract

The rapid expansion of metaverse platforms has increased the volume and complexity of digital transactions, creating a greater need for reliable anomaly detection systems. This study proposes a hybrid ensemble learning framework that integrates Isolation Forest, Autoencoder, and XGBoost using a meta learning approach to detect anomalous transactions in metaverse environments. The framework combines unsupervised and supervised learning to identify structural irregularities, behavioral deviations, and contextual patterns associated with high-risk activities. Using a transaction dataset containing behavioral, contextual, and numerical features, the hybrid model was evaluated against its individual components. The results show that the proposed framework achieves superior accuracy, precision, recall, and ROC AUC values compared to standalone models. The analysis of feature importance indicates that quantitative variables, including transaction amount, session duration, and risk score, provide the strongest predictive contribution, while contextual and behavioral factors improve model interpretability and generalization. Principal Component Analysis further visualizes the separation between normal and anomalous clusters, confirming that the hybrid ensemble effectively captures latent relationships within high-dimensional transaction data. Overall, the findings demonstrate that the proposed approach provides a robust and scalable solution for detecting irregular patterns in metaverse-based blockchain transactions. This model also offers practical implications for real-time financial risk assessment and digital security management in decentralized virtual economies.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 1 (2026): Regular Issue March 2026 Vol. 2 No. 4 (2025): Regular Issue December 2025 Vol. 2 No. 3 (2025): Regular Issue September 2025 Vol. 2 No. 2 (2025): Regular Issue June 2025 Vol. 2 No. 1 (2025): Regular Issue March Vol. 1 No. 3 (2024): Regular Issue December Vol. 1 No. 2 (2024): Regular Issue September Vol. 1 No. 1 (2024): Regular Issue June