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This Author published in this journals
All Journal Journal of Innovation Information Technology and Application (JINITA) ASEAN Journal for Science and Engineering in Materials ASEAN Journal of Educational Research and Technology
Hewa Majeed Zangana
Duhok Polytechnic University
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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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

Abstract

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

Abstract

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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Abstract

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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Abstract

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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Abstract

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.
Co-Authors Agis Abhi Rafdhi Bobi Kurniawan, Bobi Dian Dharmayanti Dostnazar Ximmataliyev Eddy Soeryanto Soegoto Goh Kang Wen Juliana Karin Marwan Oma Maulana, Hanhan Mohd. Kamir Yusof Natheer Yaseen Ali Riska Endah Septiani Senny Luckyardi Tomas Chochole