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Mitigating the Risks of Enterprise Software Upgrades: A Change Management Perspective: A Change Management Perspective Hewa Majeed Zangana; Natheer Yaseen Ali; Marwan Oma
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2404

Enterprise software upgrades are crucial for maintaining competitive advantage, ensuring security, and enhancing operational efficiency. However, these upgrades often pose significant risks, including system disruptions, data loss, and user resistance. The problem lies in effectively managing these risks to avoid operational setbacks and ensure successful adoption. This paper explores the role of change management in mitigating these risks by offering solutions through strategic planning, stakeholder engagement, and comprehensive training programs. The research employs a mixed-methods approach, integrating quantitative survey results from 185 participants and qualitative insights from 20 in-depth interviews. Results indicate that organizations prioritizing stakeholder engagement, tailored training, and proactive communication achieve higher user satisfaction, improved system performance, and enhanced operational efficiency. These findings provide a framework for best practices in change management that minimize risks and promote successful software upgrades.

Mitigating the Risks of Enterprise Software Upgrades: A Change Management Perspective: A Change Management Perspective Hewa Majeed Zangana; Natheer Yaseen Ali; Marwan Oma
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2404

Enterprise software upgrades are crucial for maintaining competitive advantage, ensuring security, and enhancing operational efficiency. However, these upgrades often pose significant risks, including system disruptions, data loss, and user resistance. The problem lies in effectively managing these risks to avoid operational setbacks and ensure successful adoption. This paper explores the role of change management in mitigating these risks by offering solutions through strategic planning, stakeholder engagement, and comprehensive training programs. The research employs a mixed-methods approach, integrating quantitative survey results from 185 participants and qualitative insights from 20 in-depth interviews. Results indicate that organizations prioritizing stakeholder engagement, tailored training, and proactive communication achieve higher user satisfaction, improved system performance, and enhanced operational efficiency. These findings provide a framework for best practices in change management that minimize risks and promote successful software upgrades.

Energy-Harvesting Materials for Autonomous Smart Farming Sensors: A Literature Review Riska Endah Septiani; Bobi Kurniawan; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Mohd. Kamir Yusof; Tomas Chochole; Hewa Majeed Zangana
ASEAN Journal for Science and Engineering in Materials Vol 6, No 1 (2027): AJSEM: Volume 6, Issue 1, March 2027
Publisher : Bumi Publikasi Nusantara

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The integration of the Internet of Things (IoT) in smart farming is hindered by limited battery life and the environmental impact of electronic waste. This review evaluates the development of energy-harvesting materials as a solution to power autonomous agricultural sensors. Through a systematic review, this paper analyzes three main mechanisms: Organic Photovoltaic (OPV), triboelectric nanogenerator/piezoelectric nanogenerator (TENG/PENG), and thermoelectric generator (TEG). Flexible polymers for TENGs and perovskite-based solar cells have the highest potential in addressing canopy shading and outdoor weather challenges. However, material toxicity and degradation due to UV and humidity remain major obstacles. Future research must prioritize biocompatible materials and hybrid systems to ensure the sustainability of precision agriculture.

Predictive Modelling of Electronic Materials: A Review of Deep Learning Techniques in Computer Engineering Agis Abhi Rafdhi; Hanhan Maulana; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Goh Kang Wen; Tomáš Chochole; Hewa Majeed Zangana
ASEAN Journal for Science and Engineering in Materials Vol 5, No 3 (2026): AJSEM: Volume 5, Issue 3, December 2026
Publisher : Bumi Publikasi Nusantara

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This review evaluates the application of deep learning (DL) for the predictive modeling of electronic materials in computer engineering. We analyzed peer-reviewed literature across four major databases, focusing exclusively on advanced architectures like Graph Neural Networks (GNNs) and Generative models. Results indicate these models accurately predict critical properties, such as band gaps and thermal conductivity, for next-generation semiconductors, 2D materials, and memristors. These high accuracies are achieved because architectures like GNNs effectively capture complex 3D spatial interactions without requiring manual feature engineering. However, practical fabrication remains hindered by data scarcity, algorithmic opacity, and a profound "Sim-to-Real Gap". While DL accelerates predictive design, sustaining Moore's Law ultimately requires developing autonomous "Self-Driving Labs" and Large Material Models to bridge digital predictions with physical synthesis.

Evolution of Artificial Intelligence (AI)-driven Information Systems in Higher Education: A Review Juliana Karin; Dian Dharmayanti; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Mohd. Kamir Yusof; Tomáš Chochole; Hewa Majeed Zangana
ASEAN Journal of Educational Research and Technology Vol 5, No 3 (2026): AJERT: VOLUME 5, ISSUE 3, December 2026
Publisher : Bumi Publikasi Nusantara

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Artificial Intelligence (AI) has fundamentally reshaped the architecture of Information Systems (IS) within higher education institutions. This systematic literature review examines the technological transition from traditional management databases to intelligent, autonomous frameworks. By analyzing peer-reviewed studies published over the last decade, this paper identifies three major evolutionary phases: the automation of administrative tasks, the rise of adaptive learning platforms, and the integration of predictive analytics for student success. The findings highlight how AI-driven systems enhance operational efficiency and personalize student experiences while simultaneously introducing complex challenges regarding data ethics and algorithmic bias. This review provides a comprehensive synthesis of current trends, offering a strategic roadmap for educators and technologists to navigate the future of intelligent academic ecosystems.

Meningkatkan Administrasi Bisnis Melalui Sistem Pendukung Keputusan: Tinjauan Komprehensif Hewa Majeed Zangana; Azar Abid Salih
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

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

Hybrid Decision Support Framework with Explainable AI and Multi-Criteria Optimization Hewa Majeed Zangana; Noor Salah Hassan; Marwan Omar; Jamal N. Al-Karaki
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

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

A Lightweight Hybrid Template-Matching–CNN Framework with Attention-Guided Fusion for Robust Small Object Detection Hewa Majeed Zangana; Marwan Omar; Mohammed Aquil Mirza; Xinwei Cao; Sharyar Wani
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14751

Small object detection in aerial and surveillance imagery remains challenging due to low resolution, occlusion, and background clutter. This study introduces a novel hybrid detection framework that fuses template matching with a deep learning detector (Faster R-CNN) through an attention-guided decision fusion mechanism. The novelty lies in (i) a dual-stage fusion pipeline that integrates precise structural cues from template matching with deep semantic features, and (ii) a custom scale-aware focal loss, adapted from Focal Loss to emphasize hard and small objects by dynamically increasing penalties for low-confidence predictions. Evaluated on a Pascal VOC subset (1000 images, 5 classes), the proposed system achieves an mAP improvement of 3.5% over the Faster R-CNN baseline and surpasses YOLO-Lite and R-CNN variants in precision and recall. The hybrid design adds only a minimal computational overhead (0.45 s/image vs. 0.42 s for Faster R-CNN), demonstrating favorable efficiency–accuracy trade-offs suitable for scalable deployment. These findings highlight the framework’s robustness, particularly in scenes containing occlusion, clutter, or visually small targets. Limitations regarding template dependency are discussed, along with future directions for automatic template generation and real-time video adaptation.

K-Nearest Neighbors for Smart Solution Transportation: Prediction Distance Travel and Optimization of Fuel Usage and Charging Recommendations for ICE Vehicles Based in Surabaya Farid Baskoro; Widi Aribowo; Hisham Shehadeh; Hewa Majeed Zangana; Wahyu Sasongko Putro; Sri Dwiyanti; Aristyawan Putra Nurdiansyah
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15068

Surabaya ranks 9th in Southeast Asia and 44th globally in the TomTom Traffic Index, with an average travel time of ±22 minutes for a 10 km distance, longer than Jakarta’s ±20 minutes. Given these traffic conditions, this study examines the application of the K-Nearest Neighbors (KNN) algorithm to predict vehicle travel distance based on remaining fuel consumption and provides recommendations for the nearest Gas Station (SPBU) based on the predicted distance. The study seeks to provide accurate distance predictions and recommend the nearest Gas Station (SPBU) for users based on fuel consumption and the predicted route, helping to navigate Surabaya’s congested traffic efficiently. The data used includes various levels of fuel consumption: 0.02, 0.06, 0.10, 0.14, 0.16, 0.20, and 0.24 liters for engines of 110, 125, and 150 cc. The model evaluation results, using three metrics: MAE, MAPE, and RMSE show that KNN performs excellently at low fuel consumption levels. At a consumption rate of 0.02 liters, the model produces a low MAE of 0.347, MAPE of 31.21%, and RMSE of 0.40, indicating minimal prediction error. The model's performance remains consistent at a consumption of 0.06 liters with MAE of 0.330, MAPE of 9.90%, and RMSE of 0.41, demonstrating a high level of accuracy. Technically, the implementation of this model can help reduce traffic congestion by directing vehicles to the nearest gas stations, thereby minimizing sudden stops on the road, improving traffic flow, and reduce wasted time spent searching for distant gas stations.

A Hybrid Quantum-Classical Optimization Model for Reconfigurable Intelligent Surfaces in 6G Networks Hewa Majeed Zangana; Maryam A. Sulaiman
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.276

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for sixth-generation (6G) wireless networks by providing programmable control over the radio propagation environment. However, optimizing RIS configurations in large-scale and dynamic 6G scenarios remains a computationally intensive and non-convex problem, particularly under realistic channel conditions involving user mobility, multi-user interference, and fading effects. This paper proposes a hybrid quantum–classical optimization framework that integrates a Variational Quantum Eigensolver (VQE)–based optimization module with classical iterative solvers to efficiently configure RIS phase shifts and reflection coefficients. The quantum component facilitates probabilistic exploration of the high-dimensional and combinatorial search space associated with large RIS deployments, while the classical component enforces system constraints and ensures convergence stability. Simulation results under realistic 6G channel models demonstrate that the proposed hybrid approach achieves up to 32% faster convergence, 18–25% improvement in spectral efficiency, and notable energy efficiency gains compared to state-of-the-art classical optimization techniques. Furthermore, the framework exhibits scalable performance with increasing RIS element counts and user density, highlighting its suitability for near real-time RIS control under noisy intermediate-scale quantum (NISQ) hardware constraints. These findings indicate that hybrid quantum–classical optimization constitutes a practical and scalable solution for intelligent, adaptive, and energy-efficient RIS-assisted 6G networks.

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