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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 35 Documents
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Optimizing Gait-Based Biometric Authentication in the Metaverse Using Random Forest and Support Vector Machine Algorithms Limbong, Tonni; Simanullang, Gonti; Silitonga, Parasian D.P.
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.37

Abstract

This paper investigates the potential of gait-based authentication for securing virtual environments, specifically within the Metaverse. With the growing need for reliable and secure identity verification in virtual spaces, traditional authentication methods, such as passwords or PINs, have proven insufficient. In contrast, biometric authentication systems, including gait analysis, provide a more secure and user-friendly alternative by leveraging unique physiological and behavioral traits for identity verification. This research applies machine learning algorithms—Random Forest and Support Vector Machine (SVM)—to gait data for distinguishing between authentic users and imposters. The dataset consists of 1,000 simulated gait samples with 16 features, such as stride length, step frequency, joint angles, and ground reaction forces (GRF). After performing exploratory data analysis (EDA), including feature distribution visualization and correlation analysis, two models were trained on the data. The Random Forest model outperformed the SVM model, achieving an accuracy of 56% and a recall of 76%, indicating its effectiveness in identifying authentic users. Despite the promising results, both models showed only marginal improvement over random guessing, highlighting the need for further optimization. This study contributes to the growing body of research on gait-based biometric systems by demonstrating their potential as a viable method for identity verification in virtual environments. It also identifies the most important gait features, such as step frequency, cadence variability, and knee joint angle, that significantly contribute to the classification process. Future research should explore advanced deep learning techniques and the integration of multimodal biometric systems to enhance the performance and reliability of gait-based authentication.
Clustering Player Performance in Pokémon TCG Tournaments: A K-means Approach to Identifying Performance Groups Based on Wins, Losses, and Tournament Statistics Sembina, Gulbakyt; Naizabayeva, Lyazat
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.38

Abstract

This study applies K-means clustering to analyze player performance in competitive Pokémon TCG tournaments, categorizing players into four distinct performance groups based on metrics such as wins, losses, and ties. Using a dataset comprising over 186,000 players, the study identifies key clusters representing varying levels of success in the game. The data was preprocessed by handling missing values and standardizing features to ensure uniform contribution across metrics. MiniBatchKMeans was employed to optimize clustering for large datasets, resulting in a model that groups players into low, moderate, and high-performance categories. The clustering results provide valuable insights into the distribution of player performance and help identify trends in competitive dynamics. A Silhouette Score of 0.4582 indicates that the clustering is moderately effective, with some overlap between clusters, suggesting that further refinement may be needed. Visualizations, including scatter plots, box plots, and heatmaps, were used to interpret the cluster characteristics, showing that top-performing players cluster into smaller groups, while a large majority of players exhibit moderate performance. The findings offer important implications for both players and tournament organizers: players can refine strategies based on their cluster profiles, while organizers can use clustering insights to design more balanced and engaging tournament formats. Future research could explore alternative clustering methods and incorporate additional performance features to further optimize player segmentation and enhance tournament design.
Predicting Player Performance in Valorant E-Sports using Random Forest Algorithm: A Data Mining Approach for Analyzing Match and Agent Data in Virtual Environments Paramita, Adi Suryaputra; Jusak, Jusak
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.39

Abstract

This study presents a data-driven approach to predict player performance in Valorant, an increasingly popular e-sport, using a Random Forest machine learning model. As e-sports continue to evolve within the metaverse, the need for strategic optimization and player selection has become critical. By analyzing a dataset containing player statistics from the Valorant Champion Tour (VCT), we aimed to predict player Rating, a key performance indicator. The dataset includes various metrics such as Kills Per Round, Average Combat Score (ACS), Clutch Success Ratio, and Kills:Deaths. After preprocessing the data, which involved handling missing values and feature engineering, the dataset was split into training and testing sets (80% and 20%, respectively). The Random Forest model, with 100 estimators and a maximum depth of 10, was trained on the processed data. The model's performance was evaluated using regression metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The results demonstrated that the model could predict player performance with a high degree of accuracy, with an R² value of 0.8831, meaning it explained 88.31% of the variance in player ratings. Furthermore, Kills Per Round emerged as the most significant feature, followed by Kill, Assist, Trade, Survive Ratio and Average Damage Per Round. These insights suggest that key metrics like kills and damage output are crucial for predicting player success. This study not only provides a comprehensive framework for predicting Valorant player performance but also demonstrates the potential of data mining in optimizing e-sports strategies. The findings contribute to the growing body of research on virtual gaming environments and offer actionable insights for teams in the metaverse, enabling data-driven decision-making to enhance performance and strategic outcomes.
Predicting Roblox Game Popularity Using Random Forest Algorithm: A Data Mining Approach to Analyze the Impact of Player Engagement and Game Features Abdulaziz, Ezzat Mansour; Bazarah, Mohammad Ahmad O.
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.40

Abstract

This study explores the use of the Random Forest algorithm to predict the popularity of Roblox games based on key player engagement metrics such as Active players, Likes, Dislikes, Favourites, and Rating. Using a dataset of the top 1000 games on Roblox, the model was trained to predict the total number of Visits for each game, serving as the target variable. The model was evaluated using multiple metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), with an R² score of 0.7814, indicating a strong ability to predict game popularity. Key findings include the significant role of Dislikes in determining game success, which had the highest importance score in the model, followed by Likes and Active players. These insights suggest that negative feedback, as captured by Dislikes, plays an important role in shaping a game's visibility and success, alongside positive engagement metrics. Despite the promising results, the study acknowledges limitations such as reliance on publicly available data and the potential for data sparsity in less popular games. The study contributes to the understanding of metaverse game dynamics, specifically on platforms like Roblox, by providing a robust predictive model that can aid game developers in optimizing their games for better player engagement and long-term success. Future research directions include incorporating additional player behavior data and testing alternative machine learning models to further enhance predictive accuracy and address the limitations of this study.
Price Trend Prediction and Discount Optimization for Video Games in Online Stores Using XGBoost and Time-Series Analysis: A Data Mining Approach for Metaverse-Driven Market Insights Maidin, Siti Sarah; Yahya, Norzariyah
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.41

Abstract

This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.

Page 4 of 4 | Total Record : 35

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