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Journal of Current Research in Blockchain
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30481430     DOI : https://doi.org/10.47738/jcrb
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
The Journal of Current Research in Blockchain publishes high-quality research on: Blockchain technology Smart Contract Data Privacy Decentralization Data Distributed Ledger Technology Decentralized Applications Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by blockchain technology across various sectors.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 1 No. 2 (2024): Regular Issue September" : 5 Documents clear
Volatility Comparison of Dogecoin and Solana Using Historical Price Data Analysis for Enhanced Investment Strategies Yadulla, Akhila Reddy; Maturi, Mohan Harish; Nadella, Geeta Sandeep; Satish, Snehal
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.13

Abstract

This study compares the volatility of two prominent cryptocurrencies, Dogecoin (DOGE) and Solana (SOL), using historical price data spanning five years from June 3, 2019, to June 3, 2024. By leveraging detailed daily trading information, the analysis provides a comprehensive understanding of the risk profiles associated with each cryptocurrency. The methodology involves data preprocessing, exploratory data analysis (EDA), volatility calculation using 30-day rolling windows, and statistical testing, including two-sample t-tests and variance ratio tests. The findings indicate that both DOGE and SOL exhibit significant price variability, with SOL showing higher average prices and greater standard deviation compared to DOGE. For instance, the mean closing price for DOGE was $0.0875 with a standard deviation of $0.0941, while SOL had a mean closing price of $54.6754 and a standard deviation of $59.3020. Historical volatility trends reveal distinct patterns: DOGE’s volatility is primarily influenced by social media trends and speculative trading, whereas SOL’s volatility is driven more by technological advancements and market developments. The two-sample t-test results show no significant difference in the mean volatilities of DOGE and SOL (t-statistic: -0.8674, p-value: 0.3858), but the variance ratio test highlights that SOL’s volatility is significantly more variable than DOGE’s, with a variance ratio of 10.7028. These results suggest that while the average risk levels of DOGE and SOL are similar, their volatility behaviors differ significantly. For investors, understanding these distinct volatility characteristics is crucial for making informed decisions regarding asset allocation and risk management. The study's insights also provide valuable guidance for financial analysts and portfolio managers, emphasizing the importance of considering both average volatility and its variability when assessing the risk profiles of cryptocurrencies. Future research should explore the impact of external factors such as regulatory changes and macroeconomic events on cryptocurrency volatility and expand the analysis to include other digital assets and longer time periods. Incorporating high-frequency trading data and advanced econometric models could further enhance the accuracy of volatility predictions, offering deeper insights into the behavior of digital currencies under various market conditions
Economic Decentralization through Blockchain Opportunities Challenges and New Business Models Berlilana; Mu’amar, Arif
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.14

Abstract

Blockchain technology has emerged as a transformative force with the potential to decentralize economic systems and create innovative business models. This paper explores the opportunities and challenges associated with economic decentralization through blockchain, focusing on the development and sustainability of new business models such as Decentralized Finance (DeFi) platforms and Decentralized Autonomous Organizations (DAOs). The study employs a qualitative research design, incorporating a comprehensive literature review and detailed case studies of prominent blockchain-based platforms. The findings highlight the significant potential of blockchain to democratize access to financial services, enhance transparency, and reduce reliance on intermediaries. However, the study also identifies critical challenges that must be addressed for blockchain to achieve widespread adoption. These include scalability issues, regulatory uncertainty, and security vulnerabilities, all of which pose significant risks to the sustainability of blockchain-based business models. A SWOT analysis is conducted to provide a structured evaluation of these strengths, weaknesses, opportunities, and threats, offering insights into the strategic position of blockchain in various industries. The analysis reveals that while the opportunities for innovation and disruption are vast, the path to realizing these benefits is fraught with technical, legal, and operational challenges. The paper concludes that ongoing research, technological advancements, and regulatory clarity will be essential to unlocking the full potential of blockchain technology in driving economic decentralization.
Predictive Modeling of Blockchain Stability Using Machine Learning to Enhance Network Resilience Hery; Widjaja, Andree E.
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.15

Abstract

Blockchain technology is widely recognized for its security, transparency, and decentralization, yet ensuring the stability of blockchain networks as they scale remains a significant challenge. This study introduces a novel approach by integrating machine learning models to evaluate and predict blockchain stability, offering a proactive solution to maintain network reliability. The primary objective was to identify the key factors influencing stability and assess the effectiveness of different machine learning models in predicting instability events. Using a dataset derived from blockchain transaction data and network metrics, we applied Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) neural networks, and K-Means Clustering algorithms. The LSTM model demonstrated the highest accuracy (94.3%) and an AUC-ROC of 0.952, significantly outperforming other models in predicting stability events. The Random Forest model revealed that transaction throughput and network latency are the most critical factors, contributing 35.2% and 28.1% to network stability, respectively. Additionally, K-Means Clustering identified three distinct stability patterns, each representing different risk levels, providing actionable insights for network management. The key contribution of this research lies in the integration of machine learning into blockchain management, presenting a novel approach that enhances the predictability and resilience of blockchain systems. The findings suggest that machine learning can be effectively employed to develop early warning systems, enabling timely interventions to prevent network instability. This study not only advances the understanding of blockchain stability but also offers practical solutions for its enhancement, marking a significant step forward in the field. Future work should focus on the real-time implementation of these models and the exploration of more advanced techniques to further improve predictive capabilities.
Enhancing Security and Efficiency in Decentralized Smart Applications through Blockchain Machine Learning Integration Hayadi, B Herawan; El Emary, Ibrahiem M. M.
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.16

Abstract

This study investigates the integration of machine learning (ML) into blockchain-based smart applications, aiming to enhance security, efficiency, and scalability. The research contributes a novel framework that combines blockchain's decentralized ledger with privacy-preserving ML techniques, addressing key challenges in data integrity and computational efficiency. The primary objective is to evaluate the performance of this integration in a simulated smart grid environment, focusing on security, processing time, energy consumption, and scalability. Our findings reveal that the integrated system significantly improves security, achieving a 98% success rate in mitigating data breaches and reducing the impact of adversarial attacks by 90%. Computational efficiency is also enhanced, with the optimized blockchain-ML configuration reducing processing time by 33% and energy consumption by 20% compared to standard blockchain setups. However, scalability remains a challenge; the system demonstrates effective scalability up to 100 nodes, beyond which transaction processing time increases by 50%, indicating the need for further optimization. The results suggest that while the integration of ML and blockchain offers substantial improvements in security and efficiency, addressing scalability and environmental impact are critical for broader application. The novelty of this research lies in its dual focus on enhancing both security and efficiency within blockchain-ML systems, providing a foundation for future advancements in decentralized intelligent applications across industries. This work contributes to the field by offering empirical data that supports the viability of blockchain-ML integration and by highlighting the areas where further research is needed to realize its full potential.
Anomaly Detection in Blockchain Transactions within the Metaverse Using Anomaly Detection Techniques Henderi; Siddique, Quba
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.17

Abstract

The rapid expansion of blockchain technology and its integration into the Metaverse has brought about significant opportunities, but also new challenges, particularly in ensuring the security and integrity of transactions. This study explores the application of anomaly detection techniques, specifically the Isolation Forest algorithm, to identify unusual and potentially fraudulent transactions within a blockchain dataset. The analysis focuses on detecting anomalies across various transaction types, such as sales and scams, and regions including Asia and Africa. The dataset, comprising 78,600 transactions, revealed that 3,930 (approximately 5%) were flagged as anomalies. "Sale" and "Scam" transactions were found to be particularly vulnerable, accounting for the majority of anomalies. Geographical analysis highlighted that Asia and Africa had the highest average risk scores, indicating a higher prevalence of high-risk transactions in these regions. Visualizations further emphasized the distribution of anomalous activities, providing valuable insights into regional and transaction-specific risks. The study demonstrates the effectiveness of Isolation Forest in detecting anomalies within blockchain transactions and underscores the importance of targeted security measures. The findings suggest that focusing on high-risk transaction types and regions can enhance blockchain security. Future research is encouraged to explore additional anomaly detection methods and integrate network analysis to further refine the detection of suspicious activities in decentralized networks. This research contributes to the growing body of knowledge on blockchain security, offering practical insights for improving the detection and mitigation of risks in the increasingly complex and interconnected world of the Metaverse.

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