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Global Science: Journal of Information Technology and Computer Science
Published by International Forum of Researchers and Lecturers
ISSN : 31089976     EISSN : 31089968     DOI : 10.70062
Core Subject : Science,
Computer Science & IT
Global Science: Journal of Information Technology and Computer Science; This a journal intended for the publication of scientific articles published by International Forum of Researchers and Lecturers This journal contains studies in the fields of Information Technology and Computer Science, both theoretical and empirical. This journal is published 1 year 4 times (March, June, September and December)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 14 Documents
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Digital Twin-Driven Cybersecurity Risk Assessment Model for Industrial Internet of Things (IIoT) Networks in Manufacturing 4.0 Atika Mutiarachim; Royke Lantupa Kumowal; Nigar Aliyeva
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.175

Abstract

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.
Enhancing Cross-Organizational Healthcare Analytics Through Blockchain-Enabled Federated Learning Mutiara S. Simanjuntak; Aji Priyambodo; Elshad Yusifov
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.176

Abstract

This study explores the integration of blockchain technology with federated learning (FL) to enhance cross-organizational healthcare analytics while ensuring privacy and data security. Federated learning allows multiple institutions to collaboratively train machine learning models without sharing sensitive patient data. Instead, local data is used to train models, and only model parameters are exchanged. However, privacy concerns and data sharing inefficiencies have hindered broader healthcare collaboration. Blockchain, a decentralized ledger technology, addresses these concerns by ensuring data integrity and transparency, providing an immutable and tamper-proof record of all transactions. This study investigates how the combination of blockchain and federated learning can overcome these challenges, facilitating secure and efficient data sharing between healthcare institutions. The study uses synthetic multi-institution healthcare datasets to simulate real-world collaboration scenarios. The blockchain-enabled federated learning system ensures that no raw patient data is shared, significantly reducing the risk of privacy breaches while still allowing healthcare institutions to collaborate on predictive model development. The results show that while there is a slight decrease in model accuracy compared to centralized methods, the trade-off is outweighed by the privacy and security benefits. Blockchain’s integration ensures that model updates are transparent, enhancing trust between institutions and reducing concerns about data integrity. Moreover, the use of blockchain’s smart contracts automates and enforces compliance, further streamlining collaboration. This research contributes to the field by demonstrating how blockchain-integrated federated learning can create a secure, scalable, and privacy-preserving framework for collaborative healthcare analytics. The findings underscore the potential for this approach to enhance healthcare outcomes and improve decision-making across institutions while ensuring patient data protection.
Augmented Reality-Assisted Explainable AI Platform for Collaborative Design of Cyber-Physical Systems in Industrial Automation Anjun Dermawan; Efan Efan; Elay Yusifli Elshad
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.177

Abstract

The integration of Augmented Reality (AR) and Explainable AI (XAI) within Cyber-Physical Systems (CPS) design is transforming the industrial automation landscape. This study explores how combining AR’s immersive visualization with XAI’s decision transparency enhances collaborative design processes in CPS. The AR-XAI platform developed in this research improves team collaboration by offering real-time visual feedback and enabling interactive decision-making. The platform provides interpretable insights into AI-driven decisions, fostering trust among engineers and decision-makers. Key features of the platform include the ability to visualize complex CPS models, facilitating faster iterations, reducing design errors, and improving design accuracy. The integration of XAI ensures transparency in decision-making by offering clear explanations of AI predictions, which is essential for ensuring accountability and building trust in automated systems. Testing with industrial engineers confirmed that the AR-XAI platform significantly improved design outcomes, with a reduction in errors and enhanced team performance compared to traditional design methods. Moreover, the platform enabled faster decision-making and improved collaboration across diverse teams, demonstrating its potential to optimize CPS design workflows. This research provides valuable insights into the role of AR and XAI in advancing Industry 4.0 and paves the way for more advanced integrations of these technologies in industrial settings. Future research should focus on developing scalable solutions for various industrial applications and exploring more sophisticated AR-XAI integrations for emerging fields like smart cities and autonomous manufacturing.
Quantum-Inspired Meta-Blockchain Consensus Algorithm for Green Cloud Data Centers Optimizing Energy and Latency Trade-Offs Ricky Imanuel Ndaumanu; Suprayuandi Pratama; Gulay Yusifli Elshad
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.178

Abstract

The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.

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