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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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Analysis of the Relationship Between Trading Volume and Bitcoin Price Movements Using Pearson and Spearman Correlation Methods Hananto, Andhika Rafi; Sugianto, Dwi
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.8

Abstract

This study investigates the relationship between trading volume and Bitcoin price movements using Pearson and Spearman correlation methods. The aim is to determine if trading volume can reliably predict Bitcoin price changes. Using a comprehensive dataset of daily Bitcoin prices and trading volumes, various statistical techniques were employed. Pearson and Spearman correlation analyses revealed very weak and statistically insignificant relationships, with correlation coefficients of -0.023788 and 0.021093, respectively. Linear regression analysis further supported these findings, showing an insignificant regression coefficient for trading volume and a very low R-squared value of 0.000566. Volatility analysis, measured by the standard deviation of daily returns, demonstrated high price volatility, consistent with the cryptocurrency market's nature. This volatility is influenced by factors such as market sentiment, regulatory developments, and macroeconomic events. The study also utilized 30-day moving averages to smooth short-term fluctuations and highlight long-term trends in trading volume and closing prices, revealing underlying trends not visible in daily data. A 1-day lagged correlation analysis indicated a very weak relationship (0.008145) between trading volume on one day and price changes on the next, suggesting other factors drive price movements. Visualizations, including time series graphs, histograms, moving averages, and volatility graphs, further illustrated the lack of a clear pattern between trading volume and price changes. In conclusion, trading volume is not a significant predictor of Bitcoin price movements, highlighting the need for comprehensive analytical approaches considering multiple variables to understand and predict Bitcoin price dynamics better.
Study of Bitcoin Market Efficiency Using Runs Test and Autocorrelation Sukmana, Husni Teja; Khairani, Dewi
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.9

Abstract

This paper presents a comprehensive statistical analysis of Bitcoin's daily returns, focusing on their unique characteristics and implications for financial modeling and market behavior. The descriptive statistics reveal a mean daily return of 0.001912 and a standard deviation of 0.044069, highlighting high volatility. The skewness of -1.297892 and kurtosis of 22.099740 indicate a left-skewed, leptokurtic distribution with frequent extreme price movements. The Jarque-Bera test statistic of 95428.68, with a p-value of 0.0, strongly rejects the null hypothesis of normality, suggesting that traditional financial models assuming normally distributed returns may be inappropriate for Bitcoin. The ADF test statistic of -12.303, with a p-value of 7.36e-23, confirms the stationarity of Bitcoin's daily returns, validating their suitability for time series analysis techniques such as ARIMA and GARCH models. Autocorrelation analysis uncovers significant short-term predictability in Bitcoin returns, challenging the weak form of market efficiency, though this predictability diminishes over time. The Runs Test, with a z-score of 2.56 and a p-value of 0.01, further supports the presence of short-term non-random behavior. Additional visualizations, including the daily closing price plot, histogram, and boxplot of daily returns, illustrate the high volatility and substantial variability in Bitcoin's market behavior. The findings underscore the need for specialized risk management strategies and financial models tailored to the cryptocurrency market's unique dynamics. While Bitcoin offers opportunities for high returns, it also poses significant risks due to its volatile nature and frequent extreme price movements. Future research should explore advanced models accounting for heavy tails and volatility clustering and examine the impact of external factors such as regulatory changes and macroeconomic events on Bitcoin's statistical properties. Understanding these characteristics is crucial for informed investment decisions and effective trading strategies in the evolving cryptocurrency market.
Analysis of Blockchain Transaction Patterns in the Metaverse Using Clustering Techniques Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.10

Abstract

This study investigates the application of various clustering techniques on a metaverse transaction dataset to identify patterns and groupings. The clustering algorithms evaluated include K-Means, DBSCAN, Gaussian Mixture Model (GMM), Mean Shift, Spectral Clustering, and Birch. The performance of these algorithms is assessed using three metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Among these algorithms, K-Means demonstrated the best overall performance, achieving the highest Silhouette Score (0.4702) and Calinski-Harabasz Index (151946.29), as well as the lowest Davies-Bouldin Index (0.6600), indicating well-defined and compact clusters. DBSCAN, while flexible, showed lower performance with a Silhouette Score of 0.1673, a Davies-Bouldin Index of 1.0084, and a Calinski-Harabasz Index of 4231.19. GMM achieved a Silhouette Score of 0.2453, a Davies-Bouldin Index of 1.3626, and a Calinski-Harabasz Index of 23011.20. Spectral Clustering had a Silhouette Score of 0.1668, a Davies-Bouldin Index of 2.0986, and a Calinski-Harabasz Index of 11830.24. Birch achieved a Silhouette Score of 0.2363, a Davies-Bouldin Index of 1.4967, and a Calinski-Harabasz Index of 21375.76. Mean Shift could not provide valid performance metrics. Visualizations, including histograms, box plots, and count plots, provided additional insights into the distribution of numerical features and cluster characteristics. This study highlights the need for tailored clustering approaches and suggests future research directions in hybrid models as well as the impact of feature selection and scaling methods on clustering outcomes.
Analyzing Sentiment Trends and Patterns in Bitcoin-Related Tweets Using TF-IDF Vectorization and K-Means Clustering Wahyuningsih, Tri; Chen, Shih Chih
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.11

Abstract

This study conducts a comprehensive analysis of Bitcoin-related tweets to understand sentiment trends and patterns using TF-IDF vectorization and K-means clustering. The dataset, comprising 1,544 unique tweets, was collected via the Twitter API and preprocessed to remove duplicates and clean the text. Sentiment analysis revealed a distribution of 53.7% neutral, 29.7% positive, and 16.6% negative tweets, indicating a predominant neutral sentiment in the discourse. Keyword analysis identified frequent terms such as 'bitcoin' (479 occurrences), 'new' (46), 'good' (43), 'crypto' (39), and 'trade' (39). Visualizations through word clouds highlighted the specific language associated with each sentiment category, with positive tweets focusing on opportunities and innovation, while negative tweets emphasized risks and scams. Cluster analysis using K-means, with the optimal number of clusters determined by the elbow method, resulted in three distinct clusters. Cluster 0, comprising 1,346 tweets, was characterized by neutral and informative content, focusing on market updates and trading strategies. Cluster 1, with 163 tweets, contained a higher concentration of positive sentiment, highlighting positive developments and investment opportunities. Cluster 2, the smallest with 35 tweets, focused on negative sentiment, reflecting concerns about market volatility and fraudulent activities. These clusters provided a nuanced understanding of the thematic composition of Bitcoin-related tweets. The study's findings have practical implications for investors, traders, and market analysts by providing insights into market mood and sentiment trends. The integration of these findings into predictive models can enhance market prediction accuracy and develop more effective trading strategies. Despite the study's contributions, limitations such as the dataset's language and scope suggest areas for future research, including real-time sentiment analysis and the incorporation of multimodal data sources. This research advances the field of sentiment analysis in financial markets, particularly within the context of cryptocurrencies, by offering a detailed and longitudinal examination of social media sentiment.
Unsupervised Anomaly Detection in Digital Currency Trading: A Clustering and Density-Based Approach Using Bitcoin Data Hariguna, Taqwa; Al-Rawahna, Ammar Salamh Mujali
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.12

Abstract

This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
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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