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Blockchain Frontier Technology (BFRONT)
Published by Pandawan Incorporation
ISSN : 28080831     EISSN : 28080009     DOI : http//doi.org/10.34306/bfront
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Library & Information Science
Security and privacy concerning blockchain technology, Blockchain theory, applications, and evolution, Smart contracts, Optimizing blockchain performance and decentralization, Ledgers and Distributed Technologies, Advanced Numerical Algorithms, Decentralized Data Storage, Data Complexity and Workflows, Administrative aspects, Decentralized Machine Learning and AI, Blockchain Applications Databases and Data Mining.
Arjuna Subject : Umum - Umum
Articles 113 Documents
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Data Mining Techniques in Blockchain Using Machine Learning Algorithms Ankur Singh Bist; Aswadi Jaya; Agung Rizky; Maulana Arif Komara; Kgomotso Moyo
Blockchain Frontier Technology Vol. 6 No. 2 (2027): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i2.1169

Abstract

The rapid advancement of blockchain technology has generated an enormous volume of complex transaction data, creating significant challenges in data analysis, particularly in terms of scalability, noise, and anonymity. This study aims to identify effective data mining techniques and implement machine learning algorithms to enhance the performance of blockchain data analysis. A quantitative approach was employed by utilizing data mining techniques and machine learning algorithms, including Random Forest, K-Means, and Neural Network. These methods were applied to blockchain datasets obtained from Ethereum, Bitcoin, and OpenSea through several stages, namely preprocessing, feature engineering, model training, and evaluation using accuracy, precision, recall, F1-score, and Root Mean Square Error metrics. The results indicate that Random Forest demonstrates stable performance with high classification accuracy, Neural Network excel at capturing complex patterns in non-linear data, while K-Means is effective in identifying patterns through clustering. These findings suggest that each algorithm offers distinct advantages depending on the characteristics of the data and the objectives of the analysis. This study concludes that the integration of data mining techniques and machine learning algorithms can significantly improve the effectiveness of blockchain data analysis compared to traditional methods. Furthermore, the proposed integrated framework can serve as a reference for the future development of blockchain-based data analytics systems. Rather than providing a quantitative benchmark against conventional analytical approaches, this study proposes a standardized methodological framework intended to support consistent implementation, evaluation, and future comparative validation across heterogeneous blockchain analytics applications.
A Literature Review of Smart Contract Performance Across Blockchain Environments Dwi Cahyono; Shofiyul Millah; Franses Gabriela Barasa; Yasir Mustafa Kareem
Blockchain Frontier Technology Vol. 6 No. 2 (2027): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i2.1176

Abstract

Smart contracts have become an important component of blockchain ecosystems because they enable automated, transparent, and decentralized digital processes. However, their performance varies across blockchain environments due to differences in architecture, consensus mechanisms, transaction costs, scalability, security, governance, and interoperability. Existing studies often examine isolated performance indicators or individual blockchain platforms, limiting comprehensive cross-platform understanding. This study aims to comparatively analyze smart contract performance across major blockchain platforms, identify technical and contextual factors influencing performance, and synthesize emerging research trends and future research opportunities. A Structured Literature Review was conducted using literature obtained from Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and Google Scholar. Peer-reviewed English publications from 2020–2026 were selected through a PRISMA-based screening process. The reviewed studies were systematically analyzed based on blockchain platforms, application domains, performance metrics, security, governance, scalability, and interoperability. The review identifies Ethereum, BNB Chain, Solana, Polygon, Hyperledger Fabric, and Avalanche as major platforms with distinct performance characteristics and trade-offs. Throughput, latency, transaction cost, and scalability are prominent technical dimensions, while security, governance, and interoperability provide essential contextual perspectives. The findings indicate that no single platform consistently provides superior performance across all application requirements. Smart contract performance should be evaluated using a multidimensional approach rather than a single technical indicator. Future research should emphasize interoperability, Layer-2 scalability, cross-chain smart contracts, security assurance, and governance to support more comprehensive blockchain performance evaluation.
Analyzing Intelligent Smart Contract Effectiveness in Blockchain Based Digital Marketing Ecosystems Raden Roro Ayu Metarini; Nuke Puji Lestari Santoso; Mitra Trima Des Sincer Putri; Marta Rodriguez
Blockchain Frontier Technology Vol. 6 No. 2 (2027): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i2.1186

Abstract

The rapid advancement of blockchain technology has accelerated the adoption of intelligent smart contracts across various sectors due to their ability to automate transactions, enhance operational efficiency, and reduce dependence on intermediaries. However, the effectiveness of intelligent smart contracts within digital marketing ecosystems remains influenced by technical and operational factors such as automated adspend execution and fraud prevention. This study aims to explore the determinants of Intelligent Smart Contract Effectiveness in blockchain-based digital marketing systems by examining the effects of Security Capability, Interoperability, Scalability, Transparency, Trustworthiness, and Automation Intelligence. A quantitative approach was employed, with data collected through questionnaires distributed to respondents with knowledge and experience related to blockchain technology. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The findings indicate that all examined variables have positive and significant effects on Intelligent Smart Contract Effectiveness. Among these variables, Automation Intelligence exhibits the strongest influence, highlighting the critical role of intelligent automation and adaptive decision-making in improving smart contract performance. Furthermore, Security Capability, Interoperability, Scalability, Transparency, and Trustworthiness also significantly contribute to the successful implementation of intelligent smart contracts. The study concludes that the effectiveness of intelligent smart contracts in decentralized blockchain systems is determined by the combined effects of security, interoperability, scalability, transparency, trustworthiness, and automation intelligence. These findings provide both theoretical and practical contributions to blockchain research and offer valuable insights for organizations and developers seeking to improve the effectiveness of intelligent smart contract implementation across various industries.

Page 12 of 12 | Total Record : 113

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