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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 40 Documents
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Analyzing Transaction Fee Patterns and Their Impact on Ethereum Blockchain Efficiency Salem, Abdel Badeeh M; Aqel, Musbah J.
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.46

Abstract

Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r ≈ 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r ≈ 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereum’s transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The study’s insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereum’s transaction fee structure and enhance overall blockchain performance.
Correlation Between Gas Prices and Transaction Value in Ethereum Blockchain Işman, Aytekin; Sangsawang, Thosporn
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.47

Abstract

This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.
Temporal Analysis of Ethereum Blockchain Trends in Transaction Fees and Block Density Over Time Bahurmuz, Ahmed Saeed; Alyoubi, Hani Atiahallah
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.48

Abstract

Ethereum, as a leading blockchain platform, experiences high variability in transaction fees due to network congestion, gas bidding, and computational complexity. This study analyzes 10,000 Ethereum transactions to identify key factors influencing transaction fees, block density, and staking mechanisms. The results show that transaction fees vary significantly, with an average of 0.1826 ETH and a standard deviation of 0.2381 ETH, indicating substantial fluctuations. A strong positive correlation (r = 0.72) between transaction size and transaction fee confirms that larger transactions incur higher costs due to increased computational demand. Time-series analysis reveals periodic spikes in gas fees, aligning with network congestion patterns. Block density averages 1718.8% (std = 501.01%), showing that some blocks are highly congested while others are underutilized. An Isolation Forest anomaly detection model identifies 3.4% of transactions as outliers, exhibiting unusually high gas fees, which may be caused by priority-based bidding, inefficient smart contract execution, or potential fee manipulation. Further analysis demonstrates that Coin Age and Stake Reward significantly influence transaction success rates. Transactions with older coins show a 7.8% higher success rate, indicating that validators may prioritize transactions with greater historical weight. Additionally, Stake Reward positively affects the Block Generation Rate (p < 0.05), confirming its role in securing the network and optimizing transaction processing. These findings provide valuable insights for Ethereum users, developers, and validators to optimize gas fees, transaction timing, and staking incentives. While this study offers critical observations, future research should focus on real-time gas fee monitoring, deep learning-based congestion forecasting, and the impact of Layer-2 scaling solutions. Understanding Ethereum’s Proof-of-Stake (PoS) dynamics will be essential for ensuring fair transaction processing, reducing gas fees, and improving blockchain efficiency.
Temporal Pattern Analysis and Transaction Volume Trends in the Ripple (XRP) Network Using Time Series Analysis Aljohani, Riyadh Abdulhadi M; Alnahdi, Abdulaziz Amir
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.49

Abstract

This study analyzes the temporal patterns and transaction volume trends in the Ripple (XRP) network using time series analysis. The dataset comprises over 1.2 million transactions spanning three years, allowing for a comprehensive examination of long-term trends and seasonal fluctuations. Summary statistics reveal a right-skewed distribution of transaction volume, where a majority of transactions involve relatively small amounts, while a few high-value transactions contribute disproportionately to overall network activity. Time series decomposition identifies a clear upward trend in transaction volume, with notable seasonal patterns corresponding to weekly and monthly cycles. These periodic trends suggest institutional trading behaviors, liquidity management strategies, and external market influences. Comparative forecasting analysis between ARIMA and LSTM models demonstrates that LSTM achieves superior predictive accuracy, with a 30% lower Mean Absolute Error (MAE) and a 25% reduction in Root Mean Squared Error (RMSE) compared to ARIMA. These results highlight the effectiveness of deep learning in capturing non-linear transaction dynamics within the blockchain ecosystem. Furthermore, anomaly detection using Isolation Forest successfully identifies transactional irregularities, particularly during periods of high market volatility and regulatory shifts. Several anomalous transaction spikes coincide with major market events, such as sudden exchange inflows and network congestion, reinforcing the role of external factors in influencing transaction activity. These findings emphasize the need for advanced forecasting techniques and real-time anomaly detection systems to improve transaction monitoring and enhance security within blockchain networks. Future research could integrate additional on-chain metrics, off-chain factors, and alternative deep learning models to refine predictive capabilities and support more resilient blockchain analytics frameworks.
Cybersecurity and Audit Compliance in Blockchain and Their Implications for System Resilience and Transaction Errors Catamio, Francis G.; Guballo, Jayvie Ochona
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.50

Abstract

This study investigates the influence of cybersecurity indicators and audit compliance on transaction reliability and customer trust within blockchain systems. Using a dataset containing daily records of operational and security metrics, the research employs descriptive statistics, correlation analysis, and multiple linear regression to evaluate how key variables—namely security incidents, audit compliance scores, and reported cyberattacks—affect transaction errors and user trust. The analysis reveals that Security Incidents are positively correlated with Transaction Errors per Million (r = 0.64), while Audit Compliance Score (%) shows a negative correlation with transaction errors (r = -0.47) and a positive correlation with Customer Trust Index (r = 0.58). A multiple regression model indicates that approximately 68.3% of the variance in transaction errors is explained by the selected predictors (Adjusted R² = 0.683). Security Incidents are a statistically significant positive predictor (p < 0.01), and Audit Compliance Score (%) is a significant negative predictor (p < 0.05), whereas Cyber Attacks Reported show no statistically significant effect. Visual analyses further confirm these relationships: systems with higher audit compliance scores tend to exhibit fewer errors and greater user trust, while those with frequent security incidents experience higher transactional failures. These findings underscore the importance of integrating both security and audit mechanisms in blockchain risk management frameworks. Future research is recommended to incorporate additional cybersecurity dimensions and explore longitudinal trends across different blockchain architectures.
Network-Based Risk Scoring of Blockchain Nodes Using Graph Neural Networks (GNN) Widodo, Slamet; Afuan, Lasmedi
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.54

Abstract

Blockchain technology has introduced a decentralized and transparent mechanism for recording transactions; however, the increasing volume and interconnectivity of blockchain networks also raise the risk of fraudulent and high-risk activities. This study proposes a Graph Neural Network (GNN)-based framework to evaluate the risk levels of blockchain nodes by integrating both transactional attributes and structural relationships. Using a dataset of 10,000 blockchain records and approximately 412,000 edges, the network was modelled as a graph in which each node represents an address and edges denote transaction or similarity links. As baselines, Random Forest and XGBoost models were employed, achieving accuracies of 0.94 and 0.95, respectively, with F1-scores of 0.93 and 0.94. These models effectively captured individual node patterns but lacked awareness of inter-node dependencies. The proposed GNN model demonstrated the highest overall performance, with an accuracy of 0.96 and an F1-score of 0.95, by learning from both node attributes and their topological context. This approach enabled the identification of high-risk nodes that traditional models failed to detect. The results confirm that network-based learning significantly enhances the accuracy and interpretability of blockchain risk analysis. The proposed GNN framework provides a scalable foundation for real-time blockchain monitoring, anomaly detection, and governance systems, contributing to improved transparency and resilience within decentralized financial ecosystems.
Network-Based Anomaly Detection in Blockchain Transactions Using Graph Neural Network (GNN) and DBSCAN Guballo, Jayvie Ochona; Andes, Joy April C.
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.55

Abstract

The increasing volume of blockchain transactions has raised significant concerns regarding the detection of irregular and high-risk activities within decentralized financial ecosystems. Conventional anomaly detection approaches tend to focus on transactional values alone, often neglecting the structural relationships that define user interactions. This study introduces a network-based anomaly detection framework that integrates graph embedding and density-based clustering techniques to identify abnormal transaction behaviours. Using a real-world blockchain transaction dataset consisting of 1,316 unique addresses (nodes) and 2,709 transaction links (edges), a directed network model was constructed to represent the flow of digital assets between users. A Singular Value Decomposition (SVD)-based graph embedding was employed to map network structures into a two-dimensional latent space, followed by DBSCAN clustering to isolate low-density outliers. The results indicate that approximately 34 nodes, or 2.6% of the total, were classified as anomalous, exhibiting unusually high transaction volumes, disproportionate connectivity, or bridging characteristics across distinct communities. These findings demonstrate that combining topological representation learning with unsupervised clustering effectively reveals hidden patterns of irregularity within blockchain networks. The proposed framework provides a computationally efficient and interpretable foundation for future integration with advanced graph learning models, such as Graph Neural Networks (GNN), to enhance fraud detection and risk assessment in decentralized systems.
Enhancing Blockchain Security Through Smart Contract Vulnerability Classification Using BiLSTM and Attention Mechanism Rahardja, Untung; Aini, Qurotul
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.56

Abstract

The rapid adoption of blockchain technology has intensified the need for robust smart contract security mechanisms. However, traditional rule-based or static analysis tools often fail to detect context-dependent vulnerabilities embedded in complex contract logic. This study proposes a deep learning framework for automated smart contract vulnerability classification using a Bidirectional Long Short-Term Memory (BiLSTM) network integrated with an Attention Mechanism. The model was trained and evaluated on the SC_Vuln_8label.csv dataset, comprising 12,520 labelled Solidity smart contracts categorized into eight distinct vulnerability types, including Re-entrancy, Integer Overflow, and Short Address Attack. Through bidirectional contextual learning and attention-based feature weighting, the proposed model achieved 93.7% test accuracy, 0.93 precision, and a macro F1-score of 0.92, outperforming baseline models such as CNN, GRU, and standard LSTM by up to 5.3 percentage points. Attention heatmap analysis further revealed the model’s interpretability by highlighting vulnerability-prone code segments (e.g., call.value, send(), and withdraw() functions) consistent with expert-identified risk indicators. These results demonstrate that the BiLSTM + Attention framework not only enhances vulnerability detection accuracy but also provides transparent and explainable reasoning, offering a reliable foundation for AI-assisted smart contract auditing systems in blockchain security.
A Hybrid Ensemble Framework Combining Transformer Networks, CNN-LSTM, and Prophet for Multi-Horizon Bitcoin Price Prediction Using 1-Minute Time Series Data Maidin, Siti Sarah; Hemalatha, M.; Sun, Jing
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.57

Abstract

Bitcoin price forecasting at one-minute frequency presents significant challenges due to rapid volatility and noise in high-frequency markets. This study proposes a hybrid ensemble framework integrating a CNN-LSTM model, a Transformer architecture, and a Prophet-based component to perform multi-horizon prediction using 500,000 one-minute BTC/USD observations. The model is evaluated across 5-minute, 15-minute, and 30-minute horizons. The results show that the ensemble achieves the best performance for the 5-minute horizon with MAE = 41.565 USD, RMSE = 60.722 USD, and MAPE = 0.156. This outperforms the CNN-LSTM model (MAE = 47.838 USD) and the Transformer model (MAE = 53.733 USD). Performance decreases at the 15-minute horizon due to Transformer instability, where the ensemble reaches MAE = 269.347 USD and the Transformer reaches MAE = 530.429 USD. At the 30-minute horizon, performance stabilizes, with the ensemble producing MAE = 84.481 USD, close to the CNN-LSTM result (MAE = 84.186 USD) and better than the Transformer (MAE = 153.887 USD). These findings indicate that the hybrid ensemble is highly effective for ultra-short-term forecasting but requires horizon-specific tuning to remain stable for medium-range intervals.
Modeling Financial Volatility of S&P 500 ETF Using GARCH and Rolling Window Analysis Yang, Wang; Fan, Chen
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.58

Abstract

This study investigates the financial volatility of the SPDR S&P 500 ETF (SPY) using two distinct approaches the Rolling Window Volatility (20-day) and the GARCH (1,1) Approximation to analyze and compare the dynamic behavior of market risk. The analysis utilizes daily SPY price data to compute logarithmic returns and model volatility persistence over time. Descriptive statistics indicate that SPY returns exhibit volatility clustering, leptokurtosis, and negative skewness, implying that extreme market movements occur more frequently than predicted by a normal distribution. Empirical results show that both volatility measures successfully capture the cyclical nature of market risk but differ in responsiveness and interpretability. The rolling window method provides an intuitive and historical view of volatility patterns, while the GARCH (1,1) model captures conditional and time-varying volatility more effectively by incorporating both short-term shocks and long-term persistence. Comparative analysis reveals that GARCH estimates produce smoother and more adaptive volatility dynamics, making them more suitable for forecasting and real-time risk assessment. Overall, the findings confirm that volatility in financial markets is not constant but evolves dynamically in response to new information and investor behavior. The study emphasizes the importance of conditional volatility models in improving the accuracy of risk evaluation, portfolio management, and market forecasting, particularly during periods of heightened uncertainty.

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