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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2024): Regular Issue June" : 5 Documents clear
Comprehensive Analysis of Twitter Conversations Provides Insights into Dynamic Metaverse Discourse Trends Kumar, Aayush; Hananto, Andhika Rafi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.2

Abstract

The metaverse, a concept originating from science fiction, has gained substantial traction in recent years as advancements in technology have brought it closer to reality. This virtual shared space, accessed through immersive technologies like virtual reality (VR) and augmented reality (AR), has captivated the imagination of both tech enthusiasts and the general public. This study aims to explore the dynamics of the metaverse discourse by analyzing online discussions across various platforms. We employed a combination of data collection methods, including Twitter API access and web scraping, to gather a diverse dataset of tweets related to the metaverse. Subsequently, the collected data underwent extensive preprocessing to ensure consistency and prepare it for analysis. Our analysis encompassed user statistics, word analysis in tweets, hashtag analysis, and tweet distribution patterns. The findings reveal intriguing insights into user behavior, content trends, and temporal patterns within the metaverse discourse. We observed prominent usernames, geographic distributions of users, prevalent words and hashtags, as well as temporal fluctuations in tweet activity. For instance, the most common username is "Fatemeh ashoobian" with 800 users, indicating a significant presence in the metaverse community. Furthermore, the number of tweets about the metaverse per day over a certain period shows daily fluctuations with the highest peak on November 14, 2023. These insights contribute to a deeper understanding of the metaverse ecosystem and its implications for society, technology, and culture. Through our research, we aim to provide valuable insights to stakeholders across various sectors, including technology developers, policymakers, content creators, and end-users. By understanding the emergent trends and themes within the metaverse discourse, stakeholders can navigate this rapidly evolving landscape more effectively and harness its transformative potential for the benefit of humanity.
Exploring User Experience and Immersion Levels in Virtual Reality: A Comprehensive Analysis of Factors and Trends Putawa, Rilliandi Arindra; Sugianto, Dwi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.3

Abstract

Virtual Reality technology has advanced rapidly in recent years, opening up new opportunities in various fields from entertainment to education. This research aims to investigate the factors influencing users' level of immersion in VR environments. Data were collected from 500 different VR users regarding their age, gender, play duration, VR headset used, and perceived motion sickness level. Analysis was conducted to evaluate the demographic distribution of users, immersion levels, play duration, and motion sickness levels. The research findings indicate that the majority of VR users are aged between 30-40 years old, with 42% of users aged 30 to 36 and 38% aged 37 to 44. Immersion levels are predominantly moderate to high, with 48% of users reporting level 3 immersion and 28% reporting level 4 immersion. Longer play durations tend to correlate with higher immersion levels, with the average play duration being 27 minutes for users with level 4 immersion compared to 18 minutes for users with level 2 immersion. Higher motion sickness levels are associated with lower immersion levels. The average motion sickness level is 2.5 for users with level 1 immersion and 1.8 for users with level 4 immersion. Additionally, the Oculus Rift VR headset proves to be the top choice for users, with 45% of the total sample using this headset and reporting an average immersion level of 3.8. This is followed by PlayStation VR with 30% of users and an average immersion level of 3.5, and HTC Vive with 25% of users and an average immersion level of 3.6. These findings provide valuable insights into users' preferences and experiences in VR environments, as well as highlighting the importance of considering factors such as age, play duration, and VR headset type in content development and interaction design. By gaining a deeper understanding of human-computer interaction dynamics in virtual environments, this research is expected to make a meaningful contribution to the future development of VR technology.
In-Depth Analysis of Web3 Job Market: Insights from Blockchain and Cryptocurrency Employment Landscape Izumi, Calvina; Hariguna, Taqwa
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.4

Abstract

The emergence of Web3, underpinned by blockchain technology, has reshaped the digital realm, ushering in a decentralized and trustless internet paradigm. In this paper, we conduct an extensive analysis of the Web3 job market, leveraging data from 2000 job postings to delineate prevalent keywords, sought-after skills, prevalent job titles, and salary determinants. Our examination reveals compelling insights into the job landscape, showcasing the dominance of technical competencies such as Ethereum proficiency and software development expertise. Among the top skills sought by employers, Ethereum (371 occurrences), React (213 occurrences), NFT (213 occurrences), Java (205 occurrences), and Rust (102 occurrences) prominently feature. Moreover, our analysis uncovers the ascendancy of specialized roles in cybersecurity, technical leadership, and project management, which command premium compensation levels. Notably, security positions emerged as the highest paying roles (average salary: $153,295.86), followed by tech lead (average salary: $121,526.32) and operations (average salary: $120,396.55). These findings offer valuable insights for job seekers, employers, educators, and policymakers navigating the evolving Web3 job landscape. By delineating key trends and challenges, our study contributes to a nuanced understanding of the transformative potential of Web3 and its implications for the future of work.
Navigating Financial Transactions in the Metaverse: Risk Analysis, Anomaly Detection, and Regulatory Implications Srinivasan, Bhavana; Wahyuningsih, Tri
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.5

Abstract

Blockchain technology has emerged as a disruptive force in the realm of finance, offering decentralized and transparent mechanisms for conducting financial transactions. This paper explores the landscape of blockchain-based financial transactions, focusing on risk analysis, anomaly detection, regulatory frameworks, and ethical considerations. Drawing on interdisciplinary insights from finance, computer science, economics, law, and ethics, the study investigates the opportunities and challenges presented by blockchain finance. Leveraging quantitative analysis, machine learning algorithms, case studies, and regulatory reviews, the research sheds light on the complexities of blockchain ecosystems. Key findings include the importance of robust risk management strategies, the role of anomaly detection in safeguarding financial integrity, and the evolving regulatory landscape surrounding blockchain transactions. The study identifies gaps in current research and proposes avenues for future investigation, emphasizing the need for interdisciplinary approaches to address the multifaceted challenges of blockchain-based finance. Ultimately, this research aims to inform stakeholders about the implications of blockchain technology in financial transactions and foster responsible innovation and sustainable development in digital finance ecosystems.
Metaverse Dynamics: Predictive Modeling of Roblox Stock Prices using Time Series Analysis and Machine Learning Abdul Ghaffar, Soeltan; Setiawan, Wilbert Clarence
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.6

Abstract

Stock price prediction is a critical task in finance and investment, enabling investors to make informed decisions and capitalize on market opportunities. This paper explores the application of predictive modeling techniques to forecast the stock prices of Roblox Corporation, a prominent player in the gaming industry. Despite the growing interest in predictive analytics, there remains a research gap concerning the application of these techniques to specific companies, particularly within the gaming sector. To address this gap, we employ a comprehensive dataset spanning from March 2021 to June 2023, obtained from Yahoo Finance, to develop predictive models using both time series analysis and machine learning algorithms. Our analysis encompasses exploratory data analysis, model development, and evaluation, culminating in insights into Roblox's stock price dynamics and model performance. The evaluation of our predictive models reveals promising results, with a Mean Squared Error (MSE) of 1.22, Root Mean Squared Error (RMSE) of 1.10, and a high R-squared (R2) score of 0.998. These metrics indicate relatively low prediction errors and a strong explanatory power of the models in capturing the variance in Roblox's closing prices. The findings shed light on the unique challenges and opportunities in predicting stock prices within the gaming industry and contribute to the growing body of knowledge in finance and investment. Through our research endeavors, we aim to empower investors and stakeholders with actionable insights to navigate the complexities of financial markets and make informed decisions with confidence and agility.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue 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