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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah Computer Science
Mohammed, Harman Salih
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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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.
Transforming Cybersecurity Practices: A Comprehensive Approach to Protecting Digital Banking Assets Zangana, Hewa; Mohammed, Harman Salih; Husain , Mamo Muhamad
Jurnal Ilmiah Computer Science Vol. 4 No. 1 (2025): Volume 4 Number 1 July 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v4i1.51

Abstract

The rapid evolution of digital banking has introduced unprecedented security challenges, necessitating a proactive and comprehensive cybersecurity framework. This paper explores advanced strategies for safeguarding digital banking assets, integrating cutting-edge technologies such as artificial intelligence (AI), blockchain, and zero-trust architectures. By analyzing emerging threats, regulatory requirements, and best practices, this study presents a holistic approach to strengthening financial cybersecurity resilience. The findings emphasize the need for a dynamic, multi-layered security model that adapts to evolving cyber threats while ensuring compliance and user trust.
AI-Driven Fraud Detection in Digital Banking: A Hybrid Approach using Deep Learning and Anomaly Detection Mohammed, Harman Salih; Sallow, Zina Bibo; Zangana, Hewa Majeed
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): 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.v15i1.5757

Abstract

The rapid digital transformation in the banking sector has introduced new opportunities for efficiency and customer convenience but has also amplified the risks of financial fraud. Traditional fraud detection mechanisms, often reliant on static rule-based systems, struggle to keep pace with the dynamic, evolving nature of fraudulent activities. This paper proposes a novel hybrid framework that integrates deep learning models with anomaly detection techniques to enhance the accuracy, robustness, and adaptability of fraud detection in digital banking. The proposed approach leverages a deep neural network (DNN) architecture trained under supervised learning to capture complex transactional patterns and combines it with autoencoder-based unsupervised anomaly detection to uncover previously unseen fraud strategies. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications and its potential for multi-institutional deployment, enabling secure inter-bank fraud intelligence sharing without compromising data privacy. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications. This work contributes to the growing field of AI-driven financial security by addressing both detection performance and adaptability to emerging fraud behaviors.
Co-Authors Husain , Mamo Muhamad Husain, Mamo Muhamad Sallow, Zina Bibo Zangana, Hewa Zangana, Hewa Majeed