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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 JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi TIJAB (The International Journal of Applied Business) Journal of Information Technology and Computer Engineering Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Sistem Pendukung Keputusan dengan Aplikasi 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 Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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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.
From Legacy Systems to Digital Solutions: Change Management in IT Transformations Zangana, Hewa Majeed; Mohammed, Harman Salih; Husain, Mamo Muhamad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.5016

Abstract

The transition from legacy systems to modern digital solutions is a pivotal aspect of IT transformations that demands meticulous planning and execution. This study examines the role of change management in IT transformations by exploring key factors such as stakeholder engagement, risk mitigation, and alignment of technology with organizational goals. A mixed-methods research approach was employed, integrating both qualitative and quantitative methodologies. The qualitative aspect involved expert interviews and case studies from multiple industries, while the quantitative approach utilized statistical regression analysis on survey responses from IT professionals. Key performance indicators (KPIs) such as project success rates, adoption levels, and cybersecurity resilience were analyzed to assess the impact of change management strategies. The study identifies a strong correlation between agile methodologies and increased organizational adaptability, emphasizing the importance of iterative development, continuous feedback, and cross-functional collaboration. Findings highlight that integrating change management frameworks with IT project delivery enhances efficiency and reduces resistance to digital transformation. This research provides a comprehensive framework for organizations aiming to optimize their IT transition processes and maximize the benefits of digital transformation.
The Role of Change Control Boards in Ensuring Cybersecurity Compliance for IT Infrastructure Zangana, Hewa Majeed; Mustafa , Firas Mahmood; Mohammed, Ayaz Khalid; Omar , Marwan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the dynamic landscape of information technology, maintaining cybersecurity compliance is a paramount concern for organizations. Change Control Boards (CCBs) play a crucial role in this context, serving as a governance mechanism to oversee and manage changes within IT infrastructure. This paper explores the significance of CCBs in ensuring cybersecurity compliance, focusing on their functions, processes, and impact on organizational security posture. Through a comprehensive review of existing literature and case studies, the research highlights how CCBs facilitate risk assessment, enforce policy adherence, and mitigate potential threats arising from changes in the IT environment. The findings underscore the importance of structured change management and suggest best practices for integrating cybersecurity considerations into the CCB workflow. By understanding the role of CCBs, organizations can enhance their ability to safeguard sensitive data and maintain regulatory compliance in an ever-evolving threat landscape.
The Role of Large Language Models in Enhancing Cybersecurity Measures: Empirical Evidence from Regional Banking Institutions Zangana, Hewa Majeed; Mohammed, Harman Salih; Husain, Mamo Muhamad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5144

Abstract

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly influenced the cybersecurity landscape, particularly in the banking sector, where threats are increasingly sophisticated. Large Language Models (LLMs) such as OpenAI’s GPT-4 and Google’s BERT, offer novel approaches to threat detection, fraud prevention, and automated risk assessment. This paper explores the integration of Large Language Models (LLMs) in cybersecurity frameworks within financial institutions, highlighting their role in real-time anomaly detection, predictive analytics, and intelligent automation of security operations. By leveraging LLMs, banks can enhance their cybersecurity resilience, mitigate cyber threats, and improve regulatory compliance. However, challenges such as data privacy concerns, adversarial attacks, and computational resource demands must be addressed to ensure the secure and ethical deployment of these models. This study provides insights into the current applications, benefits, and limitations of Large Language Models (LLMs) in strengthening cybersecurity measures in the banking sector.
Meningkatkan Administrasi Bisnis Melalui Sistem Pendukung Keputusan: Tinjauan Komprehensif Zangana, Hewa Majeed; Salih, Azar Abid
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1138

Abstract

Decision Support Systems (DSS) are critical tools in modern business administration, aiding in data analysis, decision-making, and strategic planning, the evolution of DSS has been driven by advancements in technology, increasing the complexity and volume of data businesses handle, understanding the impact of DSS on business processes and outcomes is essential for leveraging their full potential. To review and synthesize existing research on the impact of Decision Support Systems on business administration, and to identify key benefits, challenges, and best practices associated with the implementation and use of DSS in business settings. Conducted a comprehensive literature review of academic journals, industry reports, and case studies on DSS in business administration, also analyzed data from studies focusing on different aspects of DSS, including implementation strategies, technological advancements, and their effects on decision-making processes. DSS significantly improve decision-making efficiency and accuracy by providing timely and relevant information, successful implementation of DSS is associated with enhanced strategic planning, better resource allocation, and improved overall business performance, common challenges include high implementation costs, complexity of integration with existing systems, and the need for ongoing user training and support. Decision Support Systems play a pivotal role in enhancing business administration by transforming data into actionable insights. Businesses that effectively implement and utilize DSS can achieve competitive advantages through improved decision-making capabilities. Future research should focus on addressing the challenges of DSS implementation and exploring emerging technologies that can further enhance their effectiveness
Small Object Detection in Medical Imaging Using Enhanced CNN Architectures for Early Disease Screening Zangana, Hewa Majeed; Omar, Marwan; Li, Shuai; Al-Karaki, Jamal N.; Vitianingsih, Anik Vega
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14015

Abstract

Early detection of subtle pathological features in medical images is critical for improving patient outcomes but remains challenging due to low contrast, small lesion size, and limited annotated data. The research contribution is a hybrid attention-enhanced CNN specifically tailored for small object detection across mammography, CT, and retinal fundus images. Our method integrates a ResNet-50 backbone with a modified Feature Pyramid Network, dilated convolutions for contextual scale expansion, and combined channel–spatial attention modules to preserve and amplify fine-grained features. We evaluate the model on public benchmarks (DDSM, LUNA16, IDRiD) using standardized preprocessing, extensive augmentation, and cross-validated training. Results show consistent gains in detection and localization: ECNN achieves an F1-score of 88.2% (95% CI: 87.4–89.0), mAP@0.5 of 86.8%, IoU of 78.6%, and a low false positives per image (FPPI = 0.12) versus baseline detectors. Ablation studies confirm the individual contributions of dilated convolutions, attention modules, and multi-scale fusion.However, these gains involve higher computational costs (≈2× training time and increased memory footprint), and limited dataset diversity suggests caution regarding generalizability. In conclusion, the proposed ECNN advances small-object sensitivity for early disease screening while highlighting the need for broader clinical validation and interpretability tools before deployment.
Hybrid Decision Support Framework with Explainable AI and Multi-Criteria Optimization Zangana, Hewa Majeed; Hassan, Noor Salah; Omar, Marwan; Al-Karaki, Jamal N.
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1328

Abstract

Decision-making in domains such as healthcare, finance, and smart systems demands frameworks that combine model-driven expertise with data-driven adaptability. This paper proposes a hybrid decision support framework that integrates Explainable AI (XAI) with multi-criteria optimization to enhance transparency, robustness, and adaptability. Unlike traditional systems, our approach unifies mechanistic models with machine learning and embeds interpretability and optimization mechanisms. Comparative evaluation against state-of-the-art methods shows consistent performance gains, achieving 15–25% lower error rates compared with data-driven baselines and generating more diverse Pareto-optimal solutions. These improvements highlight the framework’s potential as a reliable, explainable, and scalable solution for complex, real-world decision-making
Exploring Image Representation and Color Spaces in Computer Vision: A Comprehensive Review Zangana, Hewa Majeed; Mohammed , Ayaz Khalid; Sallow , Zina Bibo; Mustafa , Firas Mahmood
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3998

Abstract

This paper presents a comprehensive review of image representation and color spaces in the domain ofcomputer vision. Image representation serves as the foundation of computer vision systems, encompassingtechniques such as pixel-based, vector-based, and feature-based representations. Color spaces provide astandardized framework for encoding color information in digital images, with popular models includingRGB, HSV, Lab, and CMYK. The paper explores fundamental concepts, comparative analysis, practicalapplications, and future directions in image representation and color spaces. Insights gained from the reviewhighlight the significance of these concepts in various computer vision applications, including objectrecognition, image segmentation, and image enhancement. Future research directions include addressingchallenges such as achieving color constancy and developing adaptive color space selection techniques. Byleveraging the findings from this review, researchers and practitioners can advance the state-of-the-art incomputer vision and develop more robust and effective systems for real-world applications.
Review of Hybrid Denoising Approaches in Face Recognition: Bridging Wavelet Transform and Deep Learning Zangana, Hewa Majeed; Mustafa , Firas Mahmood
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4209

Abstract

Statistically, image denoising is one of the key pillars of image processing and picture acquisition, which also is utilized to clear the noisy images. Over the last years, there is an increase of study subjects that are devoting to designing and making noise cancellation methods. This study reviews all major image denoising techniques, with a special emphasis on integrated deep learning approaches as well as traditional signal processing methods. The review presents a broad array of techniques for instance convolutional neural networks (CNNs), wavelet transforms, hybrid models, and their emendations. The lecturer will focus on the advantages, as well as the disadvantages, of each methodology along with their appropriateness in various fields, from which the current state of the art image denoising can be concluded. On the other hand, the paper discusses critical barriers leading to further prospects of research in cybersecurity and cybercrime prevention This review is important in that it aims to serve researchers, practitioners, and enthusiasts who would like to peer into the new trends and developments in denoise image generation.
Co-Authors Abdulazeez, Adnan M Abdullah , Dilovan Taha Abdullah, Dilovan Taha Abualigah, Laith Ahmad Khumaeni Al-Karaki, Jamal N. Ali , Natheer Yaseen Ali, Natheer Yaseen Amaliah, Fithrotul Irda Anastasia Lidya Maukar Anik Vega Vitianingsih As’ad Shidqy Aziz Ayaz khalid Mohammed B, Nur Vidia Laksmi Bazeed, Sameer Mohammed Salih Beitollahi, Hakem Cao, Xinwei DWI CAHYONO Firmansyah, Deden Hassan, Noor Salah Husain, Mamo Muhamad Li, Shuai Lidya Maukar, Anastasia Ma'rifani Fitri Arisa Mahmood Mustafa, Firas Mirza, Mohammed Aquil Mohammed , Ayaz Khalid Mohammed, Aquil Mirza Mohammed, Ayaz Khalid Mohammed, Harman Salih Muhamad, Sabat Salih Mustafa , Firas Mahmood Mustafa, Firas Mahmood Natheer Yaseen Ali Nurkholish Majid Obeyd, Salah Ramadan Oliva, Diego Omar , Marwan Omar, Marwan Omar, Naaman Rizal, Moch Arif Samsul Salih, Azar Abid Sallow , Zina Bibo Sallow, Amira Bibo Sallow, Zina Bibo Salsabilah, Azka Slamet Riyadi, Slamet Riyadi Sulaiman, Maryam A. Wani, Sharyar Widi Aribowo Zeebaree, Subhi R. M.