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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 30 Documents
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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.
A Study of Known Vulnerabilities and Exploit Patterns in Blockchain Smart Contracts Astriratma, Ria
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.40

Abstract

Blockchain smart contracts are pivotal to decentralized applications, yet their security remains a critical challenge. This study analyzes a dataset of 1,000 smart contracts to investigate known vulnerabilities, audit practices, and exploit patterns. The results reveal that audited contracts are significantly less prone to exploitation, with 75% exhibiting no exploit history compared to 55% of non-audited contracts. "Integer Overflow" and "Unchecked Call" were identified as the most prevalent vulnerabilities, contributing to 60% and 50% exploit rates, respectively. The study highlights the importance of transparent audit reporting, as contracts without available reports were exploited in 35% of cases. Additionally, hidden vulnerabilities in ostensibly secure contracts underscore the evolving sophistication of blockchain threats. This research emphasizes the need for robust security practices, including stricter coding standards, comprehensive audits, and advanced vulnerability detection techniques such as formal verification and machine learning. Future works aim to integrate security tools into development workflows and foster industry-wide collaboration to standardize auditing practices, thereby enhancing the security and trustworthiness of blockchain ecosystems.
Analysis of Gas Fee Patterns in Blockchain Transactions - A Case Study on Ethereum Smart Contracts Paramitha, Adi Suryaputra; Tarigan, Masmur
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.41

Abstract

Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereum’s gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
Blockchain Node Classification Predicting Node Behavior Using Machine Learning Prasetio, Agung Budi; Purbo, Ono Widodo
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.42

Abstract

Blockchain technology has emerged as a secure and decentralized framework for digital transactions; however, its open and pseudonymous nature also presents significant challenges related to fraudulent activities and malicious nodes. This study investigates the application of machine learning models for blockchain node classification and fraud detection, evaluating three models: Random Forest, XGBoost, and Neural Network. The research leverages a dataset of 10,000 blockchain transactions with 16 attributes, including transaction fees, block scores, stake distribution rates, and coinage. The results demonstrate that machine learning models can effectively classify blockchain nodes with high accuracy. Among the evaluated models, the Neural Network classifier outperformed the others, achieving an accuracy of 95.3%, precision of 95.1%, recall of 95.6%, and an F1-score of 95.3%. Comparatively, XGBoost achieved an accuracy of 94.1%, while Random Forest scored 92.4%. Feature importance analysis highlighted Block Score (0.38), Transaction Fee (ETH) (0.30), and Stake Distribution Rate (0.15) as the most significant factors influencing classification outcomes. Furthermore, confusion matrix analysis revealed that the Neural Network model produced 4780 true positives and 4440 true negatives, with only 200 false positives and 580 false negatives, demonstrating its robustness in identifying fraudulent nodes. Despite these promising results, real-world deployment presents several challenges, including the evolving nature of fraudulent strategies, real-time detection requirements, and scalability concerns. Future research should explore real-time learning techniques, integration of network-based features, decentralized fraud detection mechanisms, and cross-chain anomaly detection to improve model adaptability and effectiveness. By advancing these methods, machine learning-driven fraud detection can contribute to a safer, more transparent, and resilient blockchain ecosystem.
Investigating the Relationship Between Gas Consumption and Value Transferred in Ethereum Contracts Chantanasut, Suraphan
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.43

Abstract

This study investigates the relationship between gas consumption and value transferred in Ethereum smart contracts, offering insights into resource utilization and efficiency within the blockchain ecosystem. Analyzing a dataset of 1,000 smart contracts, a moderate positive correlation r=0.45,p<0.05 was observed, indicating that higher gas consumption generally corresponds to larger financial transactions. The average gas consumption per contract was found to be 58,451,329.47 units, with a standard deviation of 20,123,456.89, highlighting significant variability in computational resource usage. Similarly, the average value transferred was 7,851.47 ETH, ranging from 0.001 ETH to over 100,000 ETH, showcasing the diverse financial applications of smart contracts. Efficiency analysis, measured as the ratio of value transferred to gas consumed, revealed an average efficiency of 0.00013 ETH per unit of gas, with some contracts achieving up to 0.01 ETH per unit of gas and others as low as 0.000007 ETH per unit of gas, reflecting varying levels of optimization. Outliers with disproportionately high gas consumption relative to value transferred were identified, suggesting inefficiencies or unique use cases. These findings underscore the importance of optimizing smart contract design to minimize gas costs and improve performance. Future research directions include functionality-specific analyses, anomaly detection, comparative studies across blockchain platforms, and exploring the economic implications of gas consumption. This work provides actionable insights for developers, researchers, and policymakers aiming to enhance the efficiency and sustainability of decentralized systems.
Stake-Based Block Generation and Its Impact on Ethereum Transaction Efficiency Haodic, Gao; Xing, Zhan
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.44

Abstract

Ethereum's transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism has significantly altered the network’s block generation process and transaction efficiency. This study investigates the impact of stake-based block generation on Ethereum’s transaction fees, block density, and overall network performance by analyzing a dataset containing 303 records of Ethereum blockchain activity. The findings reveal a strong positive correlation between block generation rate and stake reward (r = 0.78, p < 0.01) and coin stake (r = 0.74, p < 0.01), indicating that validators with larger stakes generate blocks more frequently. Additionally, transaction fees positively correlate with block density (r = 0.65, p < 0.01), suggesting that network congestion remains a key determinant of transaction costs, despite the PoS transition. Further analysis shows that Ethereum’s PoS system optimizes block space utilization, with an observed mean block density of 1393.6% and a transaction fee standard deviation of 0.12 ETH, demonstrating a more stable fee structure than PoW. The average transaction fee recorded is 0.179 ETH, with a maximum observed fee of 0.98 ETH and a minimum of 0 ETH in some cases. While PoS provides greater fee stability, minor fluctuations in fees persist due to congestion-related effects. Additionally, the mean stake reward is 0.98, suggesting a relatively stable staking incentive structure across different blocks.

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