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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 103 Documents
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Blockchain Integration for Secure Data Provenance and Interoperable Database Management Terra Saptina Maulani; Dwi Cahyono; Yansa Sendi Fadillah; Maulidya Reva Aprianti; John Edwards
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v6i1.1025

Abstract

The rapid advancement of digital technologies has led to a significant increase in data volume and complexity, while traditional database systems continue to face challenges in ensuring data security, integrity, transparency, and interoperability across platforms, resulting in higher risks of data tampering, limited audit trails, and the formation of data silos. This study aims to examine and develop a blockchain integration model with conventional database systems to strengthen secure data provenance and enhance interoperability among heterogeneous databases. This research proposes a hybrid architecture that combines on data recording using a permissioned blockchain with off data storage through Relational Database Management System (RDBMS) or Not Only SQL (NoSQL) databases, where blockchain functions as a trust layer that records data hashes, metadata, and immutable change histories, while system evaluation is conducted through security testing, data integrity assessment, auditability analysis, latency measurement, throughput evaluation, data consistency analysis, and cross-platform interoperability testing. The experimental results demonstrate that blockchain integration significantly improves data security and traceability by providing transparent and tamper-resistant audit trails, while enabling secure and consistent data exchange across systems through integration modules and API gateways, despite introducing additional performance overhead compared to conventional database systems. This study concludes that integrating blockchain with conventional database systems is an effective approach for ensuring secure data provenance and interoperable database management, offering a balanced trade-off between security, transparency, and system efficiency, and presenting strong potential for further development in large-scale distributed data environments.
Non Fungible Tokens (NFTs) Marketplaces and Their Economic Implications Semaria Eva Elita Girsang; Shaumiwaty; Muhammad Noval Aryansah; Mario Putra Sanjaya; Marta Rodriguez
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1060

Abstract

The development of blockchain technology has driven the emergence of Non Fungible Tokens (NFTs) as unique digital assets traded through specialized marketplaces, forming a new digital economic ecosystem. Despite the rapid growth of the NFTs market, issues such as price volatility, the dominance of speculative activities, and uncertainty regarding long-term economic value remain insufficiently understood in academic studies. This research aims to analyze the role of NFTs marketplaces in shaping the economic value of digital assets, identify the factors influencing NFTs price dynamics, and evaluate the economic implications of the NFTs market for creators, investors, and marketplace platforms. This study employs an empirical quantitative approach by utilizing NFTs transaction data obtained from the OpenSea API, NonFungible.com, and CryptoSlam. The variables analyzed include NFTs prices, trading volume, liquidity, creator reputation, rarity score, and asset category. Data analysis is conducted using statistical and econometric methods to identify price determinants and market dynamics. The results indicate that NFTs values are significantly influenced by scarcity levels, creator reputation, asset utility, and the visibility provided by marketplaces. Marketplaces play a crucial role in shaping liquidity and market expectations, but they also contribute to increased volatility and speculative tendencies. This study concludes that the NFTs market has the potential to generate real economic value, yet it continues to face risks related to speculation and instability. These findings contribute theoretically to the digital economics literature and provide practical implications for the development of a more sustainable NFTs ecosystem.
Self Supervised Transformers for High Dimensional Time Series Anomaly Detection Aswadi Jaya; Derlina; Qurotul Aini; Agung Rizky; Richard Evans
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1078

Abstract

This study addresses anomaly detection in high dimensional time series data within the context of Artificial Intelligence (AI) driven software development, where modern systems generate large temporal data streams and reliable monitoring remains difficult due to noise, complexity, and limited labeled anomalies. The objective of this research is to develop an effective and scalable anomaly detection framework based on self supervised transformer models that can learn meaningful temporal representations without heavy reliance on manual annotation. The proposed method applies self supervised pretraining through masked sequence reconstruction and contrastive temporal learning on large scale, unlabeled multivariate time series datasets, followed by transformer based attention mechanisms to capture long range dependencies and compute anomaly scores. Experiments are conducted using benchmark datasets and real world system log data implemented with Python based deep learning tools and transformer architectures to evaluate detection performance. The results indicate that the proposed approach improves detection accuracy and reduces false positive rates compared to traditional statistical techniques and supervised deep learning models, particularly in high dimensional and low label settings. In conclusion, integrating self supervised learning with transformer architectures provides a robust and generalizable solution for time series anomaly detection, contributing to software analytics and monitoring systems by lowering labeling costs and improving adaptability across application domains.

Page 11 of 11 | Total Record : 103

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