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All Journal The Asian Journal of Technology Management (AJTM) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Buletin Ilmiah Sarjana Teknik Elektro International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Indonesian Journal of Education and Social Science Jurnal Ilmiah Computer Science The Indonesian Journal of Computer Science Control Systems and Optimization Letters
Zangana, Hewa Majeed
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Recommendation System for Determining the Best Banner Supplier Using Profile Matching and TOPSIS Methods Vitianingsih, Anik Vega; Firmansyah, Deden; Maukar, Anastasia Lidya; Kacung, Slamet; Zangana, Hewa Majeed
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.21635

Abstract

Background: Choosing a banner supplier is a significant challenge for digital printing companies due to the various advantages offered by each supplier, often leading to selections based on subjective aspects such as price and quality. Objective: This research aims to develop a system that determines the best banner supplier to minimize production inefficiencies and maximize profits by comparing two calculation methods, Profile Matching and TOPSIS. Methods: A quantitative study was conducted using transaction data from the last six months. The parameter criteria used in this system include price, quality, delivery, availability, and payment terms. The study compares the effectiveness of Profile Matching and TOPSIS methods in identifying the best supplier. Results: The study results show that the TOPSIS method is superior, yielding 100% accuracy, 84% recall, and a 92% F1-score, outperforming the Profile Matching method. This demonstrates that the correct method and algorithm effectively provide the best alternative recommendations. Conclusion: The results indicate that using the TOPSIS method leads to more accurate and objective decisions based on predetermined criteria. The findings suggest that further research should focus on refining these methods to enhance decision-making in supplier selection.
Developed Clustering Algorithms for Engineering Applications: A Review Zangana, Hewa Majeed; Abdulazeez, Adnan M
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.11636

Abstract

Clustering algorithms play a pivotal role in the field of engineering, offering valuable insights into complex datasets. This review paper explores the landscape of developed clustering algorithms with a focus on their applications in engineering. The introduction provides context for the significance of clustering algorithms, setting the stage for an in-depth exploration. The overview section delineates fundamental clustering concepts and elucidates the workings of these algorithms. Categorization of clustering algorithms into partitional, hierarchical, and density-based forms lay the groundwork for a comprehensive discussion. The core of the paper delves into an extensive review of clustering algorithms tailored for engineering applications. Each algorithm is scrutinized in dedicated subsections, unraveling their specific contributions, applications, and advantages. A comparative analysis assesses the performance of these algorithms, delineating their strengths and limitations. Trends and advancements in the realm of clustering algorithms for engineering applications are thoroughly examined. The review concludes with a reflection on the challenges faced by existing clustering algorithms and proposes avenues for future research. This paper aims to provide a valuable resource for researchers, engineers, and practitioners, guiding them in the selection and application of clustering algorithms for diverse engineering scenarios.
Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services Zangana, Hewa Majeed; Zeebaree, Subhi R. M.
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 1 (2024): INJIISCOM: VOLUME 5, ISSUE 1, JUNE 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i1.11883

Abstract

The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing applications in cloud computing. This review focuses on AI-powered applications in distributed systems, conducting a thorough examination. Analyzing foundational studies and real-world applications, it extracts insights into the dynamic interplay between AI and distributed frameworks. Quantitative measures allow a nuanced comparison, revealing diverse contributions. The survey provides a broad overview of the state-of-the-art, spanning applications like performance optimization, security, and IoT integration. The ensuing discussion synthesizes comparative measures, significantly enhancing our understanding. Concluding with recommendations for future research and collaborations, it serves as a concise guide for professionals and researchers navigating the challenging landscape of AI-powered applications in distributed cloud computing platforms.
Navigating Project Change: A Comprehensive Review of Change Management Strategies and Practices Zangana, Hewa Majeed; Bazeed, Sameer Mohammed Salih; Ali, Natheer Yaseen; Abdullah, Dilovan Taha
Indonesian Journal of Education and Social Sciences Vol. 3 No. 2 (2024)
Publisher : Papanda Publishier

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/ijess.v3i2.735

Abstract

Project change management is a critical aspect of project management that ensures successful adaptation to evolving circumstances, requirements, and stakeholder expectations. This review paper explores various dimensions of change management within project environments, including key concepts, strategies, challenges, and future directions. Drawing upon a diverse range of literature, case studies, and examples, we examine the importance of change management in driving project success. From proactive change planning to stakeholder engagement, communication, and overcoming resistance, effective change management practices are essential for navigating the complexities of project change. Despite inherent challenges and limitations, organizations have the opportunity to leverage emerging trends such as technology integration, agile methodologies, sustainability, and cultural change to enhance their change management capabilities and drive sustainable project outcomes. As organizations continue to evolve in a dynamic world, project managers must embrace change as a constant aspect of project management, adopting proactive, adaptive, and strategic approaches to enhance resilience and drive positive change. This review paper provides valuable insights and guidance for practitioners seeking to strengthen their change management capabilities and achieve project success.
Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review Zangana, Hewa Majeed; Mohammed, Ayaz Khalid; Mahmood Mustafa, Firas
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 1 (2024): May 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i1.8217

Abstract

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.
The Human Factor in Cybersecurity: Addressing the Risks of Insider Threats Zangana, Hewa Majeed; Sallow, Zina Bibo; Omar, Marwan
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.37

Abstract

In the rapidly evolving landscape of cybersecurity, the human element remains one of the most critical and complex factors to manage. Insider threats, whether originating from malicious intent or inadvertent actions, pose significant risks to organizational security. This paper explores the multifaceted nature of insider threats, examining the motivations and behaviors that drive individuals to compromise systems. By analyzing case studies and current research, we identify key vulnerabilities and the role of organizational culture in mitigating these risks. Furthermore, we propose comprehensive strategies for detecting, preventing, and responding to insider threats, emphasizing the importance of continuous education, robust access controls, and advanced monitoring technologies. This paper aims to provide a holistic understanding of the human factor in cybersecurity and offers practical solutions to address the pervasive challenge of insider threats.
Enhancing Image Quality With Deep Learning: Techniques And Applications Zangana, Hewa Majeed; Mustafa, Firas Mahmood; Mohammed, Ayaz Khalid; Omar, Naaman
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1242

Abstract

The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.
Cloud Architectures for Distributed Serverless Computing: A Review of Event-Driven and Function-as-a-Service Paradigms Zangana, Hewa Majeed; Sallow, Zina Bibo; Omar, Marwan
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 2 (2024): November 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i2.8597

Abstract

The advent of serverless computing has revolutionized the cloud computing landscape, providing scalable, cost-effective, and flexible solutions for modern application development. This paper comprehensively reviews cloud architectures for distributed serverless computing, focusing on event-driven and Function-as-a-Service (FaaS) paradigms. This research explores the fundamental principles and benefits of serverless computing, highlighting its impact on development practices and infrastructure management. The review covers key components, including orchestration, scalability, and security, and examines leading serverless platforms and frameworks. Through critically analyzing current research and industry practices, identify challenges and propose future directions for optimizing serverless architectures. This paper aims to explain how event-driven and FaaS paradigms reshape cloud computing, enabling developers to build resilient and efficient applications without server management. Our research found that event-driven architectures in serverless computing offer significant advantages in scalability, real-time processing, and resource utilization. FaaS paradigms provide modularity, granularity, and cost-effectiveness, making them suitable for various applications. Cloud-edge collaborative architectures are crucial for achieving low-latency and high-performance serverless applications but require robust security, privacy, and resource management frameworks.
Transforming Public Management: Leveraging Distributed Systems for Efficiency and Transparency Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Indonesian Journal of Education and Social Sciences Vol. 4 No. 1 (2025)
Publisher : Papanda Publishier

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/ijess.v4i1.783

Abstract

This paper explores the integration of distributed systems in public management and its implications for governance, service delivery, and innovation. Drawing on a review of existing literature and case studies, the paper examines the applications, benefits, challenges, and opportunities associated with distributed systems in the public sector. Key findings indicate that distributed systems, such as blockchain technology, offer the potential to enhance transparency, accountability, and efficiency in public management. By providing immutable and auditable records of transactions and interactions, distributed systems can reduce the risk of corruption and fraud while streamlining operations and improving service delivery. However, challenges such as interoperability issues, data privacy concerns, and regulatory complexities pose significant hurdles to adoption. Nevertheless, the adoption of distributed systems presents opportunities for innovation and collaboration, enabling governments to develop novel solutions for governance, resource management, and service delivery. Moreover, distributed systems raise important ethical and societal considerations, emphasizing the need for inclusivity, equity, and social justice in their design and deployment. At last, while challenges remain, the integration of distributed systems holds promise for building more resilient, responsive, and inclusive governance systems that better serve the needs of citizens and society as a whole.
Enhancing IT Project Agility Through Effective Change Management Strategies Zangana, Hewa Majeed; Omar , Marwan; Ali , Natheer Yaseen; Abdullah , Dilovan Taha
The Asian Journal of Technology Management (AJTM) Vol. 18 No. 1 (2025): (In Progress Issue)
Publisher : Unit Research and Knowledge, School of Business and Management, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12695/ajtm.2025.18.1.2

Abstract

In the dynamic landscape of Information Technology (IT) projects, agility is paramount for success. This paper explores the critical role of effective change management strategies in enhancing IT project agility. By integrating change management practices with agile methodologies, organizations can better adapt to evolving requirements and unexpected challenges. This study examines key change management principles and their application within agile frameworks, highlighting best practices and case studies that demonstrate successful outcomes. The findings suggest that a proactive and flexible approach to change management not only mitigates risks but also accelerates project delivery and improves stakeholder satisfaction. Ultimately, the synergy between change management and agility can significantly enhance the performance and resilience of IT projects.
Co-Authors Abdulazeez, Adnan M Abdullah , Dilovan Taha Abdullah, Dilovan Taha Abualigah, Laith Al-Karaki, Jamal N. Ali , Natheer Yaseen Ali, Natheer Yaseen Amaliah, Fithrotul Irda Anastasia Lidya Maukar Anik Vega Vitianingsih As’ad Shidqy Aziz B, Nur Vidia Laksmi Bazeed, Sameer Mohammed Salih Beitollahi, Hakem Firmansyah, Deden Husain, Mamo Muhamad Li, Shuai Mahmood Mustafa, Firas Mohammed , Ayaz Khalid Mohammed, Aquil Mirza Mohammed, Ayaz Khalid Mohammed, Harman Salih Muhamad, Sabat Salih Mustafa , Firas Mahmood Mustafa, Firas Mahmood Obeyd, Salah Ramadan Oliva, Diego Omar , Marwan Omar, Marwan Omar, Naaman Sallow , Zina Bibo Sallow, Zina Bibo Slamet Riyadi, Slamet Riyadi Wani, Sharyar Widi Aribowo Zeebaree, Subhi R. M.