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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. 3 (2025): Regular Issue September 2025" : 5 Documents clear
Classification of Game Genres Based on Interaction Patterns and Popularity in the Virtual World of Roblox Hasanah, Uswatun; Sunarko, Budi; Hidayat, Syahroni; Rachmawati, Rina
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.30

Abstract

The rapid growth of user-generated virtual environments has elevated the importance of understanding player behavior and content dynamics in metaverse platforms. This study investigates the relationship between game genres and user engagement in Roblox, one of the largest and most interactive virtual worlds. Utilizing a dataset of over 300 game entries, we analyzed engagement metrics including visits (ranging from thousands to over 2.8 billion), likes (up to 1,000,000), favorites (up to 3.4 million), and active user counts (as high as 22,155). Descriptive statistics and correlation analysis revealed that action-oriented genres—particularly Action, Shopping, and Obby & Platformer—consistently outperform others in attracting and retaining users. The strong positive correlation between likes and favorites (r = 0.95) indicates that user satisfaction strongly predicts long-term interest, while negative feedback (dislikes) shows minimal correlation with other variables. In contrast, genres such as Education and Entertainment demonstrated significantly lower averages, with visits below 1 million, and active user counts typically under 1,000. These findings provide practical insights for developers and platform administrators seeking to optimize content strategies and offer a foundation for future research involving clustering analysis, sentiment mining, and temporal behavior modeling to enhance recommendation systems and genre personalization within metaverse ecosystems.
Temporal Analysis of Blockchain Transactions in the Metaverse Using Time Series Guballo, Jayvie Ochona
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.32

Abstract

This study aims to analyze the temporal data of blockchain transactions in the metaverse using time series analysis techniques such as ARIMA and LSTM. The primary focus of this research is to identify significant trends and time patterns in transaction activities within the metaverse. By employing ARIMA, the time series data is decomposed into trend, seasonal, and residual components, providing crucial insights into its structure. The ARIMA model demonstrated a mean absolute error (MAE) of 10,525.73, a mean squared error (MSE) of 150,247,506.45, and a root mean squared error (RMSE) of 12,259.65, indicating a reasonably good fit with some potential for improvement. To capture more complex temporal dependencies in the data, an LSTM model was also applied. The performance of the LSTM model, evaluated using RMSE, was 10.0 for the training set and 15.0 for the testing set. The higher RMSE on the testing set indicates slight overfitting, where the model fits the training data better than unseen data. However, the LSTM model showed strong capability in predicting daily transaction values with fairly high accuracy, despite some minor discrepancies between actual and predicted values. Descriptive statistical analysis of the transaction data revealed that the average daily transaction volume was 108,225.72 with a standard deviation of 8,489.47, indicating significant variability. The daily transaction range spanned from 83,052.86 to 134,869.80, reflecting a wide variation in transaction volume. The results of this study highlight the importance of temporal analysis in understanding blockchain transactions in the metaverse. Insights gained from this analysis can assist in strategic planning and decision-making within the metaverse ecosystem. By further refining model tuning and employing more advanced analysis techniques, predictive accuracy can be enhanced, providing more comprehensive insights and more accurate predictions of transaction behavior.
Data-Driven Imagination Genre Clustering of Anime Content to Inspire Culturally Rich Metaverse Spaces Chantanasut, Thaworada
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.33

Abstract

The growing demand for culturally immersive experiences in virtual environments has highlighted the importance of integrating narrative-driven content into metaverse design. This study applies a data-driven clustering approach to categorize 2,000 anime titles based on genres, themes, audience demographics, and user engagement metrics such as score, number of users, and member count. Using K-Means clustering and Principal Component Analysis (PCA), five distinct clusters were identified, each reflecting unique narrative typologies and audience preferences. The resulting clusters reveal meaningful thematic patterns: Cluster 0 emphasizes action and adventure with an average score of 8.56 and over 1 million members; Cluster 1 is centered around fantasy and supernatural elements with a dominant Shounen demographic; Cluster 2 comprises psychological and sci-fi anime with high intellectual engagement; Cluster 3 features emotionally resonant titles like romance and slice of life with the highest average score of 8.78; and Cluster 4 presents genre-diverse content with a focus on comedy and school life. PCA visualization confirmed the coherence of these groupings in two-dimensional space, and genre frequency analysis showed that Action, Comedy, and Drama were the most prevalent across the dataset. The findings offer actionable insights for culturally intelligent metaverse development, proposing each genre cluster as a thematic blueprint for designing distinct virtual environments. These results demonstrate how narrative clustering can bridge media analytics with user-centered virtual worldbuilding.
Temporal Analysis of Viewer Engagement in One Piece: Trends in IMDb Ratings Across Arcs and Time Selvaraj, Poovarasan; Yang, Qingxue
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.34

Abstract

This study examines the long-term viewer reception of One Piece, a globally influential anime series, by analyzing 1,122 episodes released between 1999 and 2023 using IMDb data. The research aims to understand how audience engagement and critical reception evolve, and how content structure, specifically narrative arcs and episode types, affects these dynamics. Quantitative analysis was conducted on episode ratings, vote counts, narrative arc segmentation, and episode classification (Canon, Filler, Semi-Filler). The findings reveal a clear upward trend in average episode ratings, increasing from 7.9 in 1999 to over 8.4 in recent years. Viewer participation also grew significantly, with total annual votes rising from 63,939 in 1999 to over 300,000 votes per year after 2020. Canon episodes achieved the highest average rating (8.22) across 996 episodes and garnered a total of 2,630,987 votes. In contrast, Filler episodes averaged 6.62 (90 episodes), while Semi-Filler episodes scored 7.14 (36 episodes). The highest-rated narrative arcs, each comprising at least five episodes, consistently achieved an average rating of 8.7 or higher and demonstrated alignment with major plot developments and emotional climaxes. These results highlight the importance of narrative relevance and continuity in maintaining audience satisfaction and fostering long-term engagement. The increasing vote volume reflects the expansion of One Piece’s global audience and the role of digital platforms in amplifying participatory behavior. This study highlights how serialized media can maintain cultural impact through strategic narrative design and evolving viewer engagement.
User Transaction Patterns in Smart Contracts Based on Call Frequency and Transfer Value Hery, Hery; Haryani, Calandra
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.35

Abstract

Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.

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