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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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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 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.
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.
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
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.
Bridging the Gap: Integrating Organizational Change Management with IT Project Delivery Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): 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.v13i5.4450

Abstract

In today's rapidly evolving technological landscape, the successful implementation of IT projects is increasingly contingent upon effective organizational change management (OCM). This research paper explores the intersection of OCM and IT project delivery, proposing a comprehensive framework that integrates these two critical domains. Through a review of existing literature and analysis of case studies, we identify key challenges and best practices for synchronizing OCM strategies with IT project management processes. Our findings reveal that the alignment of OCM with IT project delivery not only enhances project success rates but also promotes sustainable organizational transformation. This integrated approach ensures that technological advancements are supported by a well-prepared workforce, thereby minimizing resistance and maximizing adoption. The paper concludes with practical recommendations for practitioners aiming to bridge the gap between OCM and IT project delivery, ultimately fostering a more agile and resilient organizational environment.
Systematic Review of Decentralized and Collaborative Computing Models in Cloud Architectures for Distributed Edge Computing Zangana, Hewa Majeed; Mohammed, Ayaz khalid; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): 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.v13i4.4169

Abstract

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.
Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis Zangana, Hewa Majeed; Obeyd, Salah Ramadan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): 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.v13i6.4651

Abstract

This paper presents a deep learning-based system for predicting gold prices using historical data. The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. A comprehensive dataset of historical gold prices is used, and the model is trained on a sequence of past data points to predict future prices. The data is preprocessed using normalization techniques to improve the performance of the model. Experimental results demonstrate the effectiveness of the proposed model in providing accurate price predictions, offering potential utility in financial forecasting and decision-making processes. The system's performance is evaluated through visualization and statistical metrics, illustrating its capacity to track gold price trends and predict future market movements. This work contributes to the growing field of time series forecasting by applying deep learning techniques to financial markets.
Power System Stabilizer Optimization Based on Modified Black‑Winged Kite Algorithm Aribowo, Widi; Abualigah, Laith; Oliva, Diego; B, Nur Vidia Laksmi; Amaliah, Fithrotul Irda; Aziz, As’ad Shidqy; Zangana, Hewa Majeed
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14669

Abstract

This article presents a Modified Method for tuning the parameters of a power system stabilizer (PSS). This article suggests a different approach that modifies the Black Kite Algorithm (BKA). The Black Kite (BKA) method is inspired by the migratory and predatory habits of the black kite. BKA combines the Leader and Cauchy mutation strategies to improve the algorithm's capacity for global search and convergence rate. This article includes comparative simulations of the PSS objective function and transient response to verify the effectiveness of the suggested strategy. The study validates the proposed method through comparison with both conventional techniques and the original BKA. Simulation results demonstrate that, when benchmarked against competing algorithms, the proposed method consistently yields optimal performance and exhibits faster convergence in certain scenarios. Notably, it reduces undershoot and overshoot by an average of 65% and 90.22%, respectively, compared to the PSS-Lead Lag method. Furthermore, the proposed approach not only minimizes overshoot and undershoot but also achieves a significantly faster settling time.
AI-Driven Threat Intelligence on Blockchain Using Deep Learning for Decentralized Cyber Risk Prediction Zangana, Hewa Majeed; Beitollahi, Hakem; Muhamad, Sabat Salih; Mohammed, Aquil Mirza; Wani, Sharyar
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.262

Abstract

The increasing complexity of cyber threats such as advanced persistent threats (APTs), ransomware, distributed denial-of-service (DDoS), and smart contract exploits requires cybersecurity solutions that go beyond traditional centralized defenses. This paper proposes an AI-driven threat intelligence framework integrated with blockchain technology for decentralized and trustworthy cyber risk prediction. The novelty of the proposed framework lies in its hybrid architecture, where deep learning–based anomaly detection models (including LSTM and autoencoder networks) analyze real-time cybersecurity data—such as blockchain transaction logs, network activity records, and external threat intelligence feeds—while blockchain is used to securely store, validate, and share AI-generated threat intelligence in a tamper-resistant and decentralized manner. Unlike AI-only solutions that suffer from data integrity and trust issues, or blockchain-only approaches that lack intelligent threat detection, the proposed framework combines the strengths of both technologies to enhance detection accuracy and stakeholder trust. Experimental evaluation conducted in a simulated blockchain environment demonstrates a detection accuracy of 96.4%, a false positive rate of 3.6%, and effective identification of multiple attack categories, including smart contract exploits and 51% attacks. While the framework improves security and transparency for inter-organizational security teams, enterprise networks, and supply-chain partners, it also introduces challenges related to computational overhead and blockchain scalability. Overall, the results indicate that integrating AI-driven threat intelligence with blockchain offers a practical and robust solution for decentralized cybersecurity risk prediction.
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments Zangana, Hewa Majeed; Sallow, Zina Bibo; Mustafa, Firas Mahmood; Husain, Mamo Muhamad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1701

Abstract

The rapid expansion of digital ecosystems has intensified concerns about data security, privacy, and trust. Blockchain technology, characterized by its decentralized, immutable, and transparent nature, offers a transformative approach to strengthening cybersecurity. This paper examines the synergy between blockchain and cybersecurity, emphasizing how blockchain’s cryptographic foundations, consensus mechanisms, and smart contracts can mitigate cyber threats, enhance authentication, and ensure data integrity. By analyzing emerging trends, challenges, and real-world applications, this study underscores the potential of blockchain to reinforce digital trust and resilience across diverse sectors. The findings contribute to the ongoing discourse on secure digital environments by proposing an integrated framework for blockchain-based cybersecurity solutions
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.