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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
 1   2 
Web-Based Goods Ordering Information System Case Study on CV. Tytus Furniture Semarang
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 1 (2025): March : 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.v1i1.11

Abstract

The development of the business world relies on technology information Already become absolute thing.​ His height activity data transactions and exchange information in online communication media and the internet is one technology For overcome activity everyday life in the era of globalization moment This. CV. TYTUS FURNITURE SEMARANG is A moving company​ in field Furniture sales , such as : table chair room visitor . Problem sale CV products. TYTUS FURNITURE SEMARANG is limitations consumer in obtain information product , type product , price product , because consumer must come direct to company or waiting for sales to come give information about company , so No can save time and costs , turnover sale not enough in accordance hope , not yet exists processing sales and consumer data reports in a way fast and efficient. For that's system very online sales required moment This For develop something company , because system the present For give real solution​ Where system the online sales can fulfil need will transaction business online with​ easy and fast with using a MySQL database with Language PHP programming . System information This capable give information product to consumer online , and can give information products sold​​​ to consumer . Based on background behind problem above , then writer take title "WEB-BASED GOODS ORDERING INFORMATION SYSTEM ON CV. TYTUS FURNITURE SEMARANG.”
Decision Making System for Selection of Prospective Scholarship Recipients Using the Saw (Simple Additive Waighting) Method at Vocational School Bina Negara Gubug Hermawan Prayoga; Rama Deddy Irawan; Achsan Edi Winata
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 1 (2025): March : 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.v1i1.12

Abstract

The selection system at Bina Negara Gubug Vocational School Jl. KH. Hasan Anwar No.9 Gubug currently processes data on the criteria for each student for each type of scholarship. It does not yet have a database system but uses a computerized system with Microsoft Excel, so there are often delays in the selection process in preparing the selection report for scholarship recipients. This research uses the Research and Development (R & D) development model by Borg and Gall with 6 steps of development, namely Research and Information Collecting, Planning, Develop Premilinary Form of Product, Premilinary Field Testing, Main Product Revision, Main Field Testing. The scholarship selection decision support system application product uses the SAW (Simple Additive Waighting) method. Visual Basic 6.0 development software and Microsoft Access database. This system can provide a useful solution for the decision-making system for selecting scholarship recipients for schools so that a better and faster selection can be achieved.
Multiuser-Based Fertilizer Supply Information System in East Plongkowati KUD, Grobgan District Sifa Alfiana; Putri Amalia Syafitri; Rina Setiana
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 1 (2025): March : 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.v1i1.13

Abstract

KUD Plongkowati Timur is located at Jalan Godong-Karangrayung, Grobogan Regency, which apart from operating in the savings and loan business unit, is also a distributor of fertilizer products produced by PT. Petrokima Gresik and PT. Pupuk Sriwidjaja Palembang. In running the fertilizer distributor business unit at KUD Plongkowati Timur, the process of recording purchase and sales transactions is not carried out using an integrated system in the database, all data is still recorded conventionally by the administration section, while the administration section's computer functions to create reports which are copied from notes provided by cashier so that data security is not guaranteed because the database is not yet integrated. Based on the problems that exist in the East Plongkowati KUD, the author would like to propose a design of a Fertilizer Inventory application system to be taken into consideration in helping speed up the process of recording purchase and sales transactions and can provide the information needed by the East Plongkowati KUD, Grobogan Regency in the form of purchase reports, sales reports, reports. suppliers, customer reports. So the design of the fertilizer inventory information system uses the Microsoft Visual Basic 6.0 programming language with a database from Microsoft Access 2007 and the design of reports from Crystal Report 7. It is hoped that making this application can be one of the efforts that can be made to overcome problems that often occur so that it can increase work effectiveness which of course will have an impact on improving the quality of service to customers.  
Inventory Data Recording Information System Multiuser Based (Case Study on Ambarawa Regional Hospital) Gopinda Tri Anda Gurusinga; Arisfi Alma Ashofi; Rifqi Rahman Abdillah
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 1 (2025): March : 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.v1i1.14

Abstract

Ambarawa Regional General Hospital (RSUD) is a Public Service Agency Regional (BLUD) belonging to Semarang Regency which operates in the field of public health services. Ambarawa Regional Hospital is located at Jalan Kartini No. 101 Ambarawa. So far, inventory data processing. At the Ambarawa Regional General Hospital (RSUD) it is still processed manually, there are several problems. What often arises is not knowing the depreciation value of goods each month, not yet applying it database system and low inventory data security system. The author used 3 research methods for data collection in this research, namely observation, interviews and literature study. The observation method used by the author is by carry out direct practice on problems that occur. Interview conducted by the author with the employees concerned, while in the literature study the author looked for related literature with research and used as a theoretical basis. From the analysis and research results, it can be seen that the solution to the problem above is: create an Inventory Data Recording Information System that uses a programming language Microsoft Visual Basic 6.0 and Microsoft SQL Server 2000 database processing system. Procedures will consist of p5 main parts, namely inventory data collection, room data collection, inventory item placement transactions, inventory item mutation transactions and line method depreciation straight. This system is expected to increase the effectiveness of inventory data processing in hospitals Ambarawa Regional General Hospital (RSUD).
Digital Twin-Based Cyber-Physical Security Framework Incorporating AI-Driven Predictive Maintenance and Zero-Trust Architecture in Smart Grid Systems Danang Danang; Febri Adi Prasetya; Rashad Huseynaga Asgarov
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.v1i3.168

Abstract

The increasing integration and digitization of smart grid systems have exposed them to a variety of security threats, necessitating robust security measures to ensure their reliability and efficiency. This paper proposes a novel Digital Twin-Based Cyber-Physical Security Framework, incorporating AI-driven predictive maintenance and zero-trust architecture to address the evolving challenges of securing smart grids. By leveraging digital twin technology, this framework creates a real-time virtual representation of physical systems, enabling continuous monitoring and simulation for enhanced security and operational performance. Zero-trust security principles are integrated to ensure that no entity, whether inside or outside the network, is trusted by default, thus significantly reducing the risk of cyber-attacks. Additionally, AI-driven predictive maintenance enhances the framework’s reliability by proactively identifying potential failures before they occur, reducing downtime and improving system resilience. Through the development and simulation of this framework, including attack and failure scenarios, the paper demonstrates that the proposed system outperforms traditional methods in terms of anomaly detection, system downtime, and response times. The integration of predictive maintenance allows for early identification of component failures, thus enhancing the overall resilience of the grid. The zero-trust architecture further strengthens the cybersecurity posture, preventing unauthorized access and attacks. The study also identifies challenges, such as data synchronization and scalability, which must be addressed for broader implementation in large-scale smart grid systems. The findings suggest that the proposed framework could play a critical role in the future evolution of smart grid security, offering valuable insights for researchers and practitioners.
Adaptive Edge-AI Framework for Real-Time Cyber-Physical Systems in Smart Cities with Resource-Constrained IoT Devices Benny Martha Dinata; Ahmad Budi Trisnawan; Eram Abbasi
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.v1i2.170

Abstract

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.
Quantum-Inspired Meta-Heuristic Algorithm for Large-Scale Graph Neural Network Training in Distributed Cloud-Edge Environments Eka Prasetya Adhy Sugara; Nurul Azwanti; Ivy Derla
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.v1i2.171

Abstract

This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.
Explainable Deep-Reinforcement Learning Framework for Autonomous Traffic Signal Control Integrating V2X Data and Smart Infrastructure Jarot Dian Susatyono; Sofiansyah Fadli; G Thippanna
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.v1i2.172

Abstract

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.
A Hybrid Federated-Edge Learning Framework with Dynamic Model Pruning for Real-Time Anomaly Detection in Smart Manufacturing Networks Genrawan Hoendarto; Thommy Willay; Pavan Kumar
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.v1i3.173

Abstract

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.
Blockchain-Enabled Multi-Agent Reinforcement Learning for Secure Decentralised Resource Allocation in 5G/6G Network Slicing Agustinus Suradi; Muhamad Aris Sunandar; Umna iftikhar
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.v1i3.174

Abstract

The integration of blockchain technology with Multi-Agent Reinforcement Learning (MARL) presents a promising solution for optimizing resource allocation and ensuring security in decentralized network environments, particularly in 5G and 6G network slicing. This research proposes a model that combines the security features of blockchain with the adaptive, decentralized decision-making capabilities of MARL. Blockchain ensures the integrity and transparency of resource allocation by providing a secure, tamper-proof ledger for transaction validation, while MARL allows agents to dynamically allocate resources based on real-time network conditions. The simulation results demonstrate significant improvements in resource allocation efficiency, fairness among users, and resilience to cyberattacks. By combining these two technologies, the proposed model overcomes many of the challenges posed by traditional centralized systems and offers an enhanced, secure, and fair solution for resource distribution in future mobile networks. However, scalability remains a challenge, especially in large-scale networks where transaction processing and consensus overhead can create bottlenecks. Additionally, training complexity in MARL models presents computational challenges, particularly in highly dynamic network environments. The model's performance trade-offs, including the balance between high security and system overhead, are also discussed. Future research should focus on optimizing blockchain consensus mechanisms to improve scalability and enhancing MARL model training techniques to reduce computational costs and improve real-time decision-making. This integration holds significant potential for revolutionizing resource allocation in 5G and 6G networks, enabling more efficient, secure, and fair management of network resources in the increasingly complex and decentralized digital ecosystem

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