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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. 2 No. 2 (2025): Regular Issue June 2025" : 5 Documents clear
Sentiment Classification of Bitcoin-Related Tweets Using VADER: Analyzing Temporal Sentiment Trends in Cryptocurrency Markets Doan, Minh Luan
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.31

Abstract

This study explores the intricate relationship between public sentiment and Bitcoin market dynamics, leveraging sentiment analysis of Twitter data to uncover patterns in emotional discourse surrounding cryptocurrency. By analyzing sentiment trends from 2013 to 2019, the research reveals a cyclical interplay between positive and negative sentiment, often aligning with Bitcoin’s dramatic price movements. Positive sentiment peaks coincide with periods of market optimism, driven by narratives of technological innovation and mainstream adoption, while negative sentiment troughs reflect moments of fear, uncertainty, and doubt (FUD) during market corrections. Despite the observed alignment, the correlation between sentiment and Bitcoin prices remains weak, underscoring the complexity of market behavior and the influence of external factors such as macroeconomic trends and regulatory developments. The findings highlight the potential of sentiment analysis as a complementary tool for market prediction, offering valuable insights into the emotional undercurrents that shape cryptocurrency markets. This study contributes to a deeper understanding of the socio-economic and psychological dimensions of Bitcoin, providing a foundation for future research in sentiment-driven market analysis.
Evaluating the Influence of Economic Indicators on Country Risk Premiums Using Random Forest: A Comprehensive Study on Global Country Data Prompreing, Kattareeya
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.32

Abstract

This study investigates the relationships between key macroeconomic indicators—Gross Domestic Product (GDP), Unemployment Rate, and Country Risk Premium—using a combination of correlation analysis, Random Forest Regression, and data visualization techniques. The correlation matrix revealed a weak negative relationship between GDP and Country Risk Premium (r = -0.19), suggesting that economic prosperity modestly reduces perceived investment risk. Conversely, Unemployment Rate exhibited a very weak positive correlation with Country Risk Premium (r = 0.065), indicating that labor market instability may slightly increase financial risk. The Random Forest model achieved a mean squared error (MSE) of 2.55 and an R-squared value of 0.018, highlighting the limited predictive power of GDP and Unemployment Rate alone. Feature importance analysis showed that GDP accounted for 53.7% of the model's predictive power, while Unemployment Rate contributed 46.3%, underscoring the relevance of both variables. Visualizations, including scatter plots and boxplots, provided further insights into the variability and complexity of Country Risk Premium. The findings suggest that while GDP and Unemployment Rate are important predictors, additional factors such as political stability or inflation rates may be necessary to improve predictive accuracy. This study contributes to the understanding of financial risk determinants and highlights the potential of advanced modeling techniques in economic research.
Classification of Bitcoin Ransomware Transactions Using Random Forest: A Data Mining Approach for Blockchain Security Emary, Ibrahiem M. M. El; Brzozowska, Anna; Popławski, Łukasz; Dziekański, Paweł; Glova, Jozef
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.33

Abstract

The rapid evolution of ransomware attacks necessitates robust and scalable detection mechanisms to safeguard digital assets. This study leverages the Bitcoin Ransomware Dataset, comprising 2,916,697 transactions, to evaluate the effectiveness of the Random Forest algorithm in classifying ransomware-related activities. Through comprehensive preprocessing, including feature encoding and standardization, and exploratory data analysis (EDA), the dataset is prepared for modeling. The Random Forest model achieves an overall accuracy of 99%, demonstrating exceptional performance in identifying the majority class. However, challenges persist in classifying minority classes, highlighting the impact of class imbalance. Feature importance analysis reveals that attributes such as income, weight, and length play pivotal roles in the classification process. The study underscores the potential of Random Forest for ransomware detection while emphasizing the need for advanced techniques to address class imbalance and improve minority class performance.
Analyzing GPU Efficiency in Cryptocurrency Mining: A Comparative Study Using K-Means Clustering on Algorithm Performance Metrics Khosa, Joe; Olanipekun, Ayorinde
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.34

Abstract

This study employs clustering analysis to evaluate the efficiency of GPUs used in cryptocurrency mining, categorizing them into distinct groups based on computational output and power consumption. Using K-Means clustering, GPUs were grouped into three clusters: low-efficiency, moderate-efficiency, and high-efficiency. High-efficiency GPUs demonstrated superior hash rates (e.g., 104.79 Mh/s for AbelHash and 218.35 Mh/s for Autolykos2) despite higher power consumption, making them ideal for high-performance mining operations. Conversely, low-efficiency GPUs exhibited lower computational output and modest energy use, highlighting opportunities for hardware upgrades or repurposing. Visualization techniques, including scatter plots and pair plots, provided clear distinctions between clusters, while a silhouette score of 0.35 indicated moderate cluster separation, suggesting areas for further refinement. The findings offer actionable insights for optimizing hardware selection, reducing operational costs, and improving energy efficiency in mining operations. Additionally, this study underscores the importance of sustainability in cryptocurrency mining and provides a foundation for future research, including the integration of additional performance metrics, exploration of alternative clustering algorithms, and development of energy-efficient mining practices. These insights contribute to the broader goal of fostering a more sustainable and data-driven approach to cryptocurrency mining.
Analyzing Price Volatility of Hedera Hashgraph Using GARCH Models: A Data Mining Approach Izumi, Calvina; Setiawan, Wilbert Clarence; Ghaffar, Soeltan Abdul
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.35

Abstract

This study employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to analyze the volatility dynamics of Hedera Hashgraph, a prominent cryptocurrency. Using a dataset of 1,901 daily price observations, we investigate the presence of volatility clustering and the persistence of market shocks, which are hallmarks of financial markets. The GARCH(1,1) model demonstrates robust performance, with a Log-Likelihood of 2927.50, AIC of -5846.99, and BIC of -5824.79, confirming its suitability for volatility estimation. Key findings reveal significant volatility clustering, with alpha (α = 0.20) and beta (β = 0.78) indicating moderate sensitivity to recent shocks and high persistence of volatility, respectively. Visualizations of conditional volatility and historical price data highlight the inverse relationship between price stability and volatility, with high volatility periods accounting for 33% of the dataset. These insights underscore the importance of real-time volatility monitoring for risk management and investment strategies. The study concludes by suggesting future research directions, including the integration of GARCH models with machine learning techniques and the exploration of external factors influencing cryptocurrency price dynamics.

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