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International Journal of Research and Applied Technology (INJURATECH)
Published by Universitas Komputer Indonesia
ISSN : 28100662     EISSN : 28077229     DOI : https://doi.org/10.34010/injuratech.v1i2.5902
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
INJURATECH cover all topics under the fields of Computer Science, Information system, and Applied Technology. Scope: Computer Based Education Information System Database Systems E-commerce and E-governance Data mining Decision Support System Management Information System Social Media Analytic Data visualization Cloud computing platforms Distributed file systems and databases Big data technologies Data capture and storage Computer Architecture and Embedded Systems Geographic information system (GIS) Remote Sensing Software Engineering Internet and Web Applications Mobile Computing Hardware and physical security Mobile Computing Security management and policies Block chain Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 210 Documents
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Control Traffic in SDN Systems by using Machine Learning techniques: Review Askar, Shavan; Hussein, Diana; Ibrahim, Media; Aziz Mohammed, Marwan
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

Due to the rapid development of Internet and mobile communication technologies, which have spearheaded a fast growth of networking systems to become increasingly complex and diverse regarding infrastructure, devices, and resources. This requires further intelligence deployment to improve the organization, management, maintenance, and optimization of these networks. However, it is difficult to apply machine learning techniques in controlling and operating networks because of the inherent distributed structure of traditional networks. The centralized control of all network operations, holistic knowledge of the network, software-based monitoring of traffic, and updating of forwarding rules to enable the functions of (SDN) are factors that (SDN) has that facilitate the application of machine learning techniques. This study will make an extensive review of existing literature to be able to answer the research question of how machine learning techniques can be used in the context of the SDN. First, it gives a review of the foundational literature information. After this, a brief review of machine learning techniques is presented. We shall also delve into the application of machine learning techniques in the area of (SDN), with a sharp edge on traffic classification, prediction of Quality-of-Service (QoS), and optimization of routing and Quality-of-Experience (QoE) security management of the resource separately. Finally, we engage in discussions surrounding challenges and broader perspectives.
Deep Learning Security Schemes in IIoT: A Review Askar, Shavan; Hussein, Diana; Ibrahim, Media; Mohammed , Marwan Aziz
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

The Industrial Internet of Things (IIoT) is a fast-growing technology that might digitize and connect numerous industries for substantial economic prospects and global GDP growth. By the fourth industrial revolution, Industrial Internet of Things (IIoT) platforms create massive, dynamic, and inharmonious data from interconnected devices and sensors. Security and data analysis are complicated by such large diverse data. As IIoT increases, cyberattacks become more diversified and complicated, making anomaly detection algorithms less successful. IIoT is utilized in manufacturing, logistics, transportation, oil and gas, mining, metallurgy, energy utilities, and aviation. IIoT offers significant potential for industrial application development, however cyberattacks and higher security requirements are possible. The enormous volume of data produced by IoT devices demands advanced data analysis and processing technologies like deep learning. Smart assembly, smart manufacturing, efficient networking, and accident detection and prevention are possible with DL algorithms in the Industrial Internet of Things (IIoT). These many applications inspired this article on DL's IIoT potential.
Virtual Classrooms and Digital Learning: An Analysis of Metaverse in Education Nur Albar, Chepi
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

The integration of the metaverse into education has the potential to revolutionize digital learning by creating immersive, interactive, and highly engaging virtual classrooms. This study examines how metaverse technologies enhance digital learning experiences, identifying both the opportunities and challenges they present. The research utilizes a mixed-method approach, combining surveys and interviews with educators and students to assess the effectiveness of metaverse-based learning. Findings suggest that while the metaverse fosters greater student engagement and collaboration, issues such as accessibility, high implementation costs, and ethical concerns must be addressed. This study contributes to the growing discourse on the future of education in a digital age, offering insights for educators, policymakers, and technology developers
Strengthening Industrial IoT Security: An Analytical Review of Cryptographic Techniques and Blockchain-Based Solutions Fuad Hasan, Mochammad
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

The Industrial Internet of Things (IIoT) has revolutionized traditional industrial systems by increasing connectivity, automation, and data-driven decision-making. However, this increased complexity also presents major cybersecurity-related challenges, including the threat of data breaches and operational disruptions. This study aims to provide an analytical review of cryptographic techniques and blockchain-based solutions in strengthening IIoT security. Combining bibliometric analysis and Systematic Literature Review (SLR), analyzing peer-reviewed articles published between 2016 and 2023 were analyzed. The bibliometric analysis revealed significant research growth trends, key contributions from global institutions, and emerging research themes such as lightweight cryptography, blockchain-based authentication, and secure communication models for resource-constrained IIoT devices. Meanwhile, the SLR provides an in-depth synthesis of the technical approaches, benefits, limitations, and open challenges in this field. The results show that the combination of cryptography and blockchain can offer decentralized, tamper-resistant, and efficient security solutions. The study also identified an urgent need for the development of more integrated and energy-efficient security models, as well as the validation of solutions in real industrial environments. The findings are expected to provide valuable guidance for the development of more reliable and secure IIoT systems in the future.
A Review of Data Mining Techniques in the Development of Decision Support Systems Munawaroh, Silvi
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to examine the role and effectiveness of various data mining techniques in improving the performance of Decision Support Systems (DSS). Using a systematic literature review method, relevant academic papers and recent studies from the last decade were analyzed to identify common approaches, applications, and challenges. The findings show that classification, clustering, association rule mining, and anomaly detection are the most widely adopted data mining techniques in DSS development. Machine learning methods such as decision trees, neural networks, and support vector machines further contribute in improving prediction accuracy and decision quality. This discussion highlights that although data mining significantly strengthens the analytical capabilities of DSS, challenges such as data quality, model interpretability, and computational complexity remain important issues. Overall, this review underscores the importance of integrating advanced data mining approaches into DSS frameworks to support smarter, scalable and adaptable decision-making processes
Optimizing Data Capture and Storage for Research Data Management: A Study on Cloud Computing Solutions in Academic Institutions Karin, Juliana
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

The increasing volume of research data generated by academic institutions necessitates effective data management strategies to ensure data accessibility, security, and long-term preservation. This study explores the optimization of data capture and storage for research data management, focusing on cloud computing solutions within academic settings. A systematic literature review was conducted, examining studies from the past five years sourced from reputable databases such as IEEE, Springer, and Elsevier. Thematic analysis was employed to identify key trends, challenges, and best practices related to data management, cloud technologies, and storage optimization. The results of a bibliometric analysis indicated a significant upward trend in publications addressing Data Management, Cloud Computing, and Research Data Storage between 2009 and 2023, with a notable peak in 2023. Findings revealed that while cloud computing platforms offer significant advantages—such as scalability, cost-efficiency, and enhanced collaboration—challenges related to data standardization, security, and interoperability persist. Furthermore, the study highlights the growing importance of automated data capture techniques and metadata tagging in managing large datasets. Despite the transformative potential of cloud-based solutions, optimization efforts remain necessary to fully realize their benefits for research purposes. This research underscores the need for future empirical studies to test cloud solutions in real-world academic contexts and develop standardized, secure, and efficient practices for research data management. Optimizing cloud computing solutions is crucial for enabling academic institutions to meet the demands of the evolving digital research environment
Evaluating the Effectiveness of Augmented Reality (AR) Tools for Interactive Learning Experiences in Higher Education Lesmana, Rudhi
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

This study explores the application of Augmented Reality (AR) technologies to enhance interactive learning experiences in higher education environments. Through a combination of literature reviews, case study analyses, and user experience evaluations, the research examines how AR tools contribute to student engagement, conceptual understanding, and overall academic performance. Key findings reveal that AR technologies create immersive and interactive environments that significantly enhance learner motivation and participation. The integration of AR into educational settings also facilitates the visualization of complex concepts, providing students with practical and experiential learning opportunities. However, challenges such as technological limitations, the need for instructor training, and issues related to device accessibility remain critical considerations for widespread adoption. Discussions highlight the transformative potential of AR in reshaping traditional pedagogical models, promoting active learning strategies, and addressing evolving educational demands. Furthermore, the study identifies best practices for effective AR implementation, including aligning AR applications with curricular goals and ensuring ease of use for both students and educators. The conclusions emphasize the growing significance of adopting innovative technologies like AR to improve learning outcomes, foster digital competence among students, and prepare higher education institutions for the future of technology-driven education.
Leveraging Blockchain for Academic Credentialing and Student Data Management in Universities Abhi Rafdhi, Agis; Neni Hayati, Euis
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to explore the potential of blockchain technology in enhancing academic credentialing and student data management within university systems. A systematic literature review methodology was employed, analyzing peer-reviewed articles published between 2018 and 2023 sourced from databases such as Elsevier, MDPI, SpringerLink, and IEEE Xplore. The findings reveal that blockchain offers significant advantages in securing academic credentials, ensuring data authenticity, and promoting transparency in record-keeping processes. By utilizing decentralized ledgers, universities can reduce fraud, streamline verification procedures, and grant students greater control over their personal academic records. These outcomes are achieved due to blockchain’s inherent features of immutability, decentralization, and smart contract automation, which collectively eliminate traditional dependencies on centralized data authorities. Furthermore, the review highlights that while blockchain adoption presents opportunities for efficiency and trust enhancement, challenges such as technical complexity, regulatory uncertainty, and integration with existing systems must be carefully addressed. In conclusion, blockchain technology holds transformative potential for revolutionizing academic administration, but successful implementation will require strategic planning, collaboration among stakeholders, and adherence to emerging legal and ethical standards. Future research should focus on real-world pilot programs and policy development to support broader adoption across higher education institutions
Simulation-Based Learning for Hardware and Physical Security: A Case Study in Engineering Education Wulan Sari , Annisa
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

In an era where cybersecurity threats increasingly target hardware components, equipping students with practical skills in hardware and physical security is crucial. This paper presents a case study on the implementation of simulation-based learning methods within an engineering education context. By integrating interactive simulations that mimic real-world physical attacks and defense mechanisms, students are able to experience and understand critical concepts such as tamper detection, access control, and secure hardware design. The study evaluates the effectiveness of simulation-based modules compared to traditional theoretical approaches through surveys and performance assessments. Results indicate that students exposed to simulations demonstrate higher engagement, deeper conceptual understanding, and improved problem-solving skills in hardware security scenarios. This research highlights the importance of adopting innovative, hands-on learning techniques to better prepare the next generation of cybersecurity professionals
Enhanced Informed Probabilistic Road Map Algorithm with Parameter Optimization Rahmani, Salma; Rajasa, M Aria
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v5i1.16092

Abstract

This paper presents the design and performance testing of the Enhanced Informed Probabilistic Roadmap (EI-PRM) algorithm in path planning in various environments, such as simple environments, dense environments and narrow paths. This research evaluates the effectiveness of the algorithm in terms of solution cost and computation time by testing different parameter configurations, including number of sample points (nsample) and cost scaling factor (). The results show that the EI-PRM algorithm can adjust the sampling strategy based on the available information, resulting in an optimal solution with high efficiency. During the test, in a simple environment with the parameter value of nsample between 200 and 400 and parameter value between 1.2 and 1.4, the best solution cost is 344.93 and the computation time is 1.9 seconds. However, in a denser environment, the optimal solution cost reaches 141,586 with a computation time of 1.16 seconds, a parameter value of nsample 200, and a parameter value of 1.5. Furthermore, the algorithm shows good performance on narrow paths with an optimal solution cost of about 293.39 and the best computation time of 0.38 seconds at a parameter value of nsample 400 parameter 1.3. This research focuses on the importance of parameter optimization and efficient sampling strategies to improve path quality and speed up computation time. In general, the results indicate that the EI-PRM algorithm is effective for path planning under various environmental conditions. The process of the EI-PRM algorithm consists of several steps. First, sample points are created at random. In the second step, the computer will link the example locations to produce a roadmap. In the last step, the shortest path inside an ellipsoid-bounded search area will be determined. The size of the ellipsoid will increase gradually until the best path solution is found. This research is expected to contribute significantly to the development of path planning algorithms that are more efficient, faster and capable of producing high-quality paths in complex environments. This research has the potential to improve applications in transportation and logistics that require optimal path planning in order to reduce operational costs and improve safety.

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