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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 5 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2025): Regular Issue March" : 5 Documents clear
Exploring the Impact of Mixed Reality Technology on Anatomy Education for Medical Students Khosa, Joe; Olanipekun, Ayorinde
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.18

Abstract

This study investigates the effectiveness of Apple Vision Pro, a mixed reality tool, in enhancing medical students' understanding of 3D anatomical structures compared to traditional teaching methods. A quasi-experimental design was employed, involving 500 medical students who were divided into two groups: the Experiment group (n = 250), which used Apple Vision Pro, and the Control group (n = 250), which relied on conventional 2D images, textbooks, and static models. Both groups completed a pre-test to assess baseline knowledge, followed by an intervention phase over three weeks, and a post-test to measure learning outcomes. The results showed that the Experiment group demonstrated significantly greater improvement in post-test scores, with a mean improvement of 19.56 ± 5.58, nearly double the 9.40 ± 2.86 improvement observed in the Control group. Statistical analysis using an independent t-test confirmed that this difference was highly significant (t = 36.20, p < 0.0001), indicating the superior effectiveness of Apple Vision Pro in facilitating spatial visualization and comprehension of anatomical relationships. Qualitative feedback from the Experiment group further highlighted the benefits of Apple Vision Pro, including its ability to deliver an immersive, interactive, and engaging learning experience. Students reported increased motivation and a deeper understanding of anatomical structures due to the dynamic nature of the mixed reality environment. In conclusion, this study provides compelling evidence that Apple Vision Pro can transform anatomy education by addressing limitations associated with traditional teaching methods. The findings suggest that integrating mixed reality tools into medical curricula can significantly enhance learning outcomes, improve student engagement, and foster a more comprehensive understanding of complex anatomical concepts. Future research should focus on evaluating the long-term impacts of mixed reality technologies on knowledge retention and practical skill development in medical education.
Scam Detection in Metaverse Platforms Through Advanced Machine Learning Techniques Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.19

Abstract

The rapid expansion of metaverse environments has introduced novel opportunities and challenges, particularly concerning user security and trust. This study investigates the application of machine learning techniques to detect scam activities within the metaverse by analyzing user behaviors and interaction patterns. Using a comprehensive dataset, we evaluated three machine learning models—Random Forest, Support Vector Machine (SVM), and Neural Network—for their effectiveness in identifying scams. The Neural Network model achieved the highest performance, with an accuracy of 91%, a recall of 92%, and an AUC of 95%, making it the most reliable solution for this task. Feature importance analysis revealed that attributes such as the number of transactions and average transaction value significantly contribute to scam detection. Hyperparameter optimization further improved model performance, demonstrating the potential of fine-tuned architectures in handling high-dimensional datasets. Despite the Neural Network’s superior performance, its computational complexity highlights the need for lightweight implementations for real-time applications. This research contributes to the growing field of metaverse security by providing a robust framework for scam detection using machine learning. Future work should focus on expanding datasets to capture multi-platform behaviors, incorporating explainable AI (XAI) for improved interpretability, and enhancing model efficiency. These advancements will ensure safer and more trustworthy metaverse ecosystems for users worldwide.
Anomaly Detection in Open Metaverse Blockchain Transactions Using Isolation Forest and Autoencoder Neural Networks Buchdadi, Agung Dharmawan; Al-Rawahna, Ammar Salamh Mujali
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.20

Abstract

The study explores anomaly detection in blockchain transactions within the Open Metaverse, utilizing Isolation Forest and Autoencoder Neural Networks. With the rise of the Metaverse, blockchain technology has become essential for secure digital transactions. However, the decentralized nature of blockchain makes it vulnerable to various anomalies, potentially undermining trust and security in digital spaces. Isolation Forest, an unsupervised machine learning algorithm, isolates anomalies based on the assumption that anomalies are few and distinct from regular data points. Its effectiveness in handling high-dimensional data makes it suitable for real-time applications. On the other hand, Autoencoders, a type of neural network, excel in detecting anomalies through reconstruction error, identifying data points that deviate from normal patterns. The research applied these models to a simulated dataset from the Open Metaverse, including features like transaction amount, login frequency, and session duration, to capture nuanced user behaviors. Preprocessing steps, such as one-hot encoding for categorical features and standardization for numerical features, ensured data consistency for accurate modeling. The Isolation Forest achieved a precision of 0.85, while the Autoencoder slightly outperformed it with a precision of 0.87. Both models demonstrated strong AUC-ROC values, with the Autoencoder scoring 0.85 compared to Isolation Forest’s 0.82, indicating robust performance in distinguishing normal from anomalous transactions. The findings underscore the potential of both models to enhance security in blockchain-based virtual environments, with the Autoencoder showing an edge in handling complex data patterns. However, the use of simulated data presents limitations, suggesting the need for further testing with real-world Metaverse transaction data. Future research could explore integrating other advanced algorithms, such as Graph Neural Networks, to improve anomaly detection in blockchain systems.
Investigating the Impact of Gameplay Hours on Player Recommendations in Steam Games: A Comparative Analysis Using Logistic Regression and Random Forest Classifiers Durachman, Yusuf; Rahman, Abdul Wahab Abdul
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.21

Abstract

The study delves into the complex relationship between gameplay hours and player recommendations on the Steam platform, leveraging both Logistic Regression and Random Forest classifiers to analyze the data. The findings underscore a strong correlation between hours played and the likelihood of recommending a game. Specifically, longer gameplay hours generally indicate higher engagement levels, which often translate into a greater propensity for players to recommend the game. However, this trend is not universally applicable; a subset of users with high playtime did not recommend their games, highlighting that engagement alone does not guarantee satisfaction. Factors such as game quality, unmet player expectations, and individual preferences may influence these outcomes. The Logistic Regression model provided a clear linear understanding of the data, demonstrating that hours played significantly affect recommendation likelihood. Its coefficients suggested a positive relationship, making it a useful tool for interpreting the odds of recommendation changes based on gameplay hours. Nonetheless, the model's limitations became evident in its inability to capture intricate, non-linear patterns within the data. In contrast, the Random Forest classifier excelled by capturing complex interactions and offering robust predictive accuracy. This model utilized ensemble learning to analyze various decision trees, thereby revealing more nuanced insights into player behaviors. Feature importance scores derived from Random Forest confirmed that hours played was a critical variable, but also highlighted the potential significance of other factors contributing to player recommendations. Model performance metrics further reinforced these observations. The Random Forest classifier outperformed Logistic Regression in terms of accuracy (82.65% compared to 81.26%), precision, recall, and the F1-score, while also delivering a higher Area Under the Curve (AUC-ROC), indicating superior discriminative power. These results suggest that Random Forest is more suitable for capturing the multifaceted dynamics of player engagement and recommendations. This comprehensive comparison illustrates how different modeling approaches can yield valuable, yet varying, insights into gaming data.
Predicting Player Performance in EA SPORTS FC 25: A Comparative Analysis of Linear Regression and Random Forest Regression Using In-Game Attributes Prompreing, Kattareeya
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.22

Abstract

This study presents a comparative analysis of Linear Regression and Random Forest Regression models to predict player performance in EA SPORTS FC 25 using in-game attributes. The primary objective is to evaluate these models in terms of their accuracy and effectiveness in predicting player ratings based on key attributes like Ball Control, Dribbling, Defense, and Reactions. The dataset comprises 17,737 player records with multiple performance indicators, preprocessed to ensure quality data for modeling. The research process involves data exploration, model development, and evaluation using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Results indicate that the Random Forest model outperforms the Linear Regression model, achieving a lower MAE and RMSE, and a higher R² score, highlighting its ability to capture complex, non-linear relationships among player attributes. The study’s findings underscore the significance of ensemble models in gaming analytics and provide insights for gamers and developers to optimize gameplay strategies and improve game mechanics. Limitations include data constraints, and recommendations for future research suggest incorporating more diverse player data and exploring advanced algorithms.

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