Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal Aptisi Transactions on Management Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
John Edwards
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Library & Information Science Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
A Comparative Analysis of Traditional and Decentralized Storage Systems in the Digital Age Po Abbas Sunarya; Desy Apriani; Ruli Supriati; Danang Surya Budi; John Edwards
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i1.2531

Abstract

The background of this research originates from the critical role of data storage in the advancement of modern digital technology, where centralized traditional cloud storage models have become dominant due to their accessibility and ease of data management. However, the challenges faced by these models include limited scalability, high operational costs, and vulnerabilities related to data security and privacy. In response to these limitations, the InterPlanetary File System (IPFS) has emerged as a decentralized storage solution that offers an alternative approach to data storage and distribution. The objective of this study is to compare IPFS and traditional cloud storage based on four key aspects: scalability, security, cost, and performance. The methodology involves a literature review and case studies of IPFS implementation in various practical scenarios. The findings reveal that while IPFS offers superior decentralization and resistance to data censorship, it still suffers from inefficiencies in data re- trieval and challenges in large-scale adoption. The results show that traditional cloud storage demonstrates advantages in access speed and integration capabilities but remains constrained by higher costs and the risks associated with data centralization. The conclusion emphasizes the need for continued research to enhance the efficiency of IPFS and to explore hybrid storage models that integrate the benefits of both decentralized and centralized technologies.
Integrating AI and Big Data to Enhance Performance andSustainability in Hospitality Hasrul Azwar Hasibuan; Syaifuddin; Rusiadi; John Edwards
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.118

Abstract

This paper explores the impact of Big Data and Artificial Intelligence (AI) on Employee Performance and Sustainability in the hospitality industry. The paper further explains how integrating Big Data and AI can optimize operations, enhance employee efficiency, and promote sustainable practices. The research uses SmartPLS to analyze the relationships between these variables, with a focus on how Big Data and AI influence Employee Performance, which in turn contributes to Sustainability efforts. The findings, show that both Big Data and AI have significant positive effects on Employee Performance, with Big Data demonstrating a stronger impact. Moreover, Employee Performance mediates the relationship between Big Data, AI, and Sustainability, indicating that improvements in employee performance lead to better sustainability outcomes, such as resource optimization and waste reduction. The study findings align with SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production), highlighting the potential of technology to drive both economic and environmental sustainability in the hospitality sector This research contributes to understanding how the application of Big Data and AI can help hospitality businesses achieve long-term success through improved operational efficiency and sustainable practices.
Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks Aswadi Jaya; Suca Rusdian; Fitra Putri Oganda; Tuti Nurhaeni; John Edwards
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kz2w4z84

Abstract

The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.
Co-Authors Aswadi Jaya Danang Surya Budi Desy Apriani Dwi Cahyono Fitra Putri Oganda Hasrul Azwar Hasibuan Maulidya Reva Aprianti Po Abbas Sunarya Ruli Supriati, Ruli Suca Rusdian Syaifuddin Terra Saptina Maulani Tuti Nurhaeni Yansa Sendi Fadillah