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Dahlan Abdullah
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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
Core Subject : Science, Education, Social, Engineering,
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation
The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 582 Documents
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Building Collaborative Advantage in Hospital Systems: The Role of Supply Chain Collaboration, Innovation, and Digital Transformation in Class C Hospitals in Java Island Uli, Syahdani; Anindita, Rina; Eff, Aprilita Rina Yanti; Syah, Tantri Yanuar Rahmat
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1483

Abstract

This study aims to analyze the influence of transformational leadership, digital transformation, supply chain collaboration, and innovation on collaborative advantage and the performance of type C hospitals in Java, with government subsidies as a moderating variable. Using a quantitative approach and PLS-SEM analysis on 50 hospitals, the results show that most direct relationships between variables are insignificant, except for the influence of innovation on collaborative advantage and the influence of collaborative advantage on hospital performance, which are proven to be significant. In addition, government subsidies only play a significant role in strengthening the relationship between supply chain collaboration and collaborative advantage. These results confirm that collaborative advantage is a key factor in improving hospital performance, with innovation as its main driver. Meanwhile, transformational leadership and digital transformation have not shown a strong direct influence due to bureaucratic limitations and implementation readiness. These findings also indicate that government subsidies are more effective in the early stages of establishing collaborations, rather than directly improving performance. This study highlights the importance of external collaboration strategies and innovation in improving the competitiveness of public hospitals, as well as the importance of adapting global theories to the local context in developing health policies in Indonesia.
Cross-Cultural Adoption of Gamified Attendance Systems: Opportunities and Challenges for Multinational Enterprises Rahardja, Untung; Andriyansah, Andriyansah; Natalia, Elisa Ananda; Hardini, Marviola; Julianingsih, Dwi
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1077

Abstract

The increasing adoption of gamified systems in workplace settings has garnered significant attention, particularly in multinational enterprises (MNEs) seeking innovative approaches to enhance team member engagement and productivity. Background Gamified attendance systems, integrating game elements such as points, leaderboards, and rewards, present a novel strategy for improving attendance and punctuality across diverse cultural contexts. However, implementing such systems in cross-cultural settings poses unique challenges and opportunities. Objective: This study explores the factors influencing the adoption of gamified attendance systems in MNEs, focusing on cross-cultural adaptability and its implications for organisational performance. Research Method A mixed-methods approach was employed, combining qualitative interviews with HR managers and employees from diverse cultural backgrounds with a quantitative survey targeting 350 employees across 10 MNEs. Data were analysed using thematic analysis and structural equation modelling to identify cultural and organisational determinants of success. Results The findings reveal that cultural dimensions, such as power distance and individualism, significantly impact team member perceptions and engagement with gamified systems. While gamification enhanced attendance rates and morale in low-power-distance cultures, it faced resistance in high-power-distance environments. Additionally, alignment with organisational goals and transparent communication were critical for successful implementation. Conclusion: This study underscores the importance of cultural sensitivity and strategic planning in the cross-cultural adoption of gamified attendance systems. By addressing these factors, MNEs can leverage gamification to foster a more engaged and productive workforce, enhancing global operational efficiency. Future research should explore longitudinal impacts and sector-specific adaptations to optimise implementation outcomes.
Swarm Intelligence Algorithms for Resource Allocation in Renewable-Powered Smart City Infrastructures Nazar, Mustafa; Majeed, Adil Abbas; Abdul Radhi, Rafah Hassan; Jafar, Qusay Mohammed; Khalil, Baker Mohammed; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1355

Abstract

The increasing integration of renewable energy sources into urban systems necessitates the development of intelligent resource management strategies to ensure optimal and reliable power distribution. Swarm Intelligence (SI) algorithms have emerged as a promising solution for addressing the complex energy management challenges inherent in smart cities, such as generation variability, distributed loads, and the need for real-time decision-making. This paper conducts a rigorous comparative analysis of three prominent SI algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—within a simulated, renewable-powered smart city environment. Our model incorporates edge computing nodes, solar and wind generation systems, and heterogeneous urban load profiles, including residential, municipal, and electric vehicle charging demands. The study evaluates each algorithm against key performance metrics, including energy efficiency, task latency, convergence behavior, load balancing, and system fault tolerance. The results unequivocally demonstrate that PSO outperforms both ACO and ABC across most performance dimensions, exhibiting faster convergence, superior energy utilization, more effective latency management, and enhanced fault recovery capabilities. While ABC demonstrates competitive performance in flexibility and fairness, ACO shows significant limitations in time-sensitive and failure-prone scenarios. This research contributes a modular simulation framework suitable for real-time edge computing applications and offers practical guidance for deploying adaptive optimization strategies in urban energy systems. Ultimately, our findings underscore the critical importance of algorithm selection in smart city energy infrastructure and highlight the potential of swarm-based intelligence to enable scalable, resilient, and efficient resource management in the sustainable cities of the future.
Islamic Law in the Era of Artificial Intelligence: A Systematic Literature Review Sudirman, Sudirman; Sutiah, Sutiah; Supriyono, Supriyono
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1072

Abstract

This systematic literature review examines the intersection between Islamic law and artificial intelligence, aiming to identify how Shariah principles address the ethical, legal, and practical challenges posed by emerging AI technologies. The study applies the PRISMA method to analyse 67 peer-reviewed publications from 2010 to 2025 sourced from central databases. The reviewed literature is categorised into five core themes: Shariah-aligned AI ethics, AI personhood and legal responsibility, the integration of AI in Islamic finance and judiciary systems, AI-assisted fatwa issuance and ijtihad, and regulatory gaps in aligning AI with maqasid al-shariah. Findings suggest an increasing scholarly engagement with Islamic jurisprudence as a moral compass for technological governance. However, the research also reveals inconsistencies in theological interpretations and a lack of policy frameworks within Muslim jurisdictions. The review concludes that a cohesive Shariah-based framework for AI ethics is both necessary and timely, and it proposes strategic directions for future research and the development of institutional policy.
Energy-Efficient Protocols for Massive IOT Connectivity in 6G Networks Ravilla, Lokesh; Upadhyay, Satish; Louis, Magthelin Therase; Swain, Biswaranjan; Mamatha, G. N.; Mishra, Smita; Aneja, Aseem
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1449

Abstract

The possible evolution in wireless communication as it approaches sixth-generation (6G) networks highlights remarkable features, including one of device connectivity, ultra-low latency, and energy efficiency, enabling mIoT deployment. However, these features come with a myriad of challenges with the integration of billions of constrained IoT devices, especially in relation to the aforementioned energy hurdles alongside scalability and spectrum efficiency. This work focuses on energy-efficient 6G IoT networks, proposing new low-power adaptive communication protocols, emphasising power adaptive performance, dependability, and trustworthiness. The roles of key facilitators, Reconfigurable Intelligent Surfaces (RIS), Non-Orthogonal Multiple Access (NOMA), machine learning coupled with energy harvesting, and even off-grid sustainable power sources are critical for enhanced sustainable connectivity. Covering the protocol design in the physical, MAC, and network layers permits the highlighting of cross-layer optimisation IoT ecosystems in 6G and the focused attention IoT research lacks, supporting bold, environmentally sustainable infrastructure designs.
Blockchain-Enabled Secure Data Sharing Framework for Healthcare IoT Devices Jing, Qi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1520

Abstract

Medical data security challenges have increased dramatically because healthcare institutions continue to integrate more Internet of Things devices to deliver data-driven clinical services. Access control systems based on RBAC, ABAC and MAC do not meet the requirements of flexible protection and scalable and context-aware security which are needed for dynamic healthcare environments. The research objective focuses on creating a resilient decentralized access control solution which delivers secure time-sensitive access permissions in healthcare IoT systems. A blockchain-based hybrid access control framework with RBAC and ABAC provides the solution to meet this requirement. A dual mechanism of smart contracts and IPFS storage runs the model while variables and user-facing elements shift based on environmental characteristics and individual circumstances. Results from experimental evaluation show that this proposed framework delivers 96.5% access precision together with policy evaluation times below 3.2 ms and 120 ms response times while handling 74 transactions per second while remaining affordable at $2.1 and demanding 45 to 52 MB from critical system memory. The obtained results demonstrate better scalability together with enhanced performance and adaptability when compared to using ABAC, RBAC and MAC singularly. Healthcare IoT systems should implement a blockchain-based hybrid access control system as an optimal method to secure data sharing in real-time resource-constrained scenarios.
A CNN-Driven Image Analysis Approach for Accurate Detection of Plant Leaf Diseases Jha, Suresh Kumar; Misra, Yogesh; Nyayadhish, Renuka; Rawat, Manoj Kumar; K, Kiran Kumar; Neelam, Nagaveni
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1299

Abstract

Plant leaf diseases are a key concern for agriculture and result in significant loss of crop yield and economic losses globally. It is vital to efficiently and accurately detect plant diseases to properly manage crops and control their diseases. This paper demonstrates a CNN-based image analysis model to automatically identify and classify plant leaf diseases from digital images. Deep learning is used in the proposed method to spontaneously learn hierarchical features from original image data, without the use of feature engineering. The model was trained and evaluated on a collection of high-resolution healthy and diseased leaf images collected from different plant species. Preprocessing (normalisation, noise filtering, and contrast increment) and data augmentation (rotations, scale changes, and flips) were also performed on the pre-processed images, and it was expected to achieve good generalisation and reduce overfitting. The CNN architecture was optimised using transfer learning in combination with hyperparameter tuning. Evaluation experiments showed that the framework attained a classification “accuracy of 96.2%, 95.8% precision, 96.5% recall, and 96.1% F1-score”. The model proved to be robust under varying light conditions and complex background settings, demonstrating its real-world applicability. In addition, the model’s lightweight architecture supports mobile and edge computing implementation, enabling real-time and on-site diagnostic capabilities. This method provides an automated, scalable system for plant disease detection, thus enabling early intervention, reducing chemical treatment reliance, and fostering sustainable agricultural practices, fostering environmentally friendly approaches. The results demonstrated the capability of CNN systems towards transforming the plant health monitoring practices in precision agriculture.
Low-Latency Edge Computing for Real-Time Applications in Wireless Sensor Networks Mishra, Awakash; Rathour, Abhinav; Raju, Dheeravath; Patil, Shashikant; Muthiah, M.A.; Patra, Bichitrananda; Kalidhas, Aravindan Munusamy
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1490

Abstract

Real-time data processing with Edge Computing, such as Low Latency Edge Computing (LLEC), allows for functioning at the network boundary or edge, which enhances responsiveness and reduces latency in WSNs. This approach is helpful for most time-critical needs in innovative city applications, healthcare, industrial automation, and other areas where prompt actions are crucial. In contrast to conventional cloud models, LLEC processes data at the collection site to reduce the transmission time, improving bandwidth efficiency. Moreover, LLEC increases the height of scalable walls and energy efficiency by shifting the computational burden to the edge nodes. This document focuses on the most critical problems in WSNs, such as restricted resources, limited scalability, and security issues. We offer a distributed edge framework with real-time processing features and minimal security protocols to address these gaps. Localized computation at cluster heads diminishes network congestion while prolonging sensor life. This paper presents multiple case studies demonstrating LLEC's effectiveness in practical applications. Finally, the paper discusses the widening scope of research and the importance of LLEC in future distributed systems.
Advancing Startup Ecosystems Through AI-Driven Matchmaking: A Comprehensive Bibliometric Analysis Lutfiani, Ninda; Wijono, Sutarto; Rahardja, Untung; Purnomo, Hindriyanto Dwi
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1095

Abstract

This study investigates the integration of AI in streamlining the alignment process between startups and potential collaborators and partners, particularly in the Indonesian startup ecosystem. The motivation behind this research lies in the gaps and challenges startups face in efficiently connecting with suitable partners or investors. We employed a bibliometric analysis approach. This study sourced data from Scopus, analysing 515 articles and 59,412 citations published from 2018 to 2023. Key findings provide insights into the predominant role of AI technologies, notably machine learning methods like deep learning and data mining, and the significance of recommendation systems that incorporate collaborative filtering. Furthermore, the results underscore the increasing importance of AI as an indispensable tool in the startup landscape, enhancing the efficiency and productivity of collaborations. We assessed publications from several countries, authors, and citations through the bibliometric measures to comprehensively understand the current trends and trajectories. The study concludes by recognising the transformative potential of AI in fostering tighter and more efficient alliances within the startup ecosystem, laying the groundwork for future research into refining AI-driven collaborative processes.
Factory-Grade Diagnostic Automation for GeForce and Data Centre GPUs Lulla, Karan; Chandra, Reena; Ranjan, Kishore
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1089

Abstract

The growing deployment of Graphics Processing Units (GPUs) across data centers, AI workloads, and cryptocurrency mining operations has elevated the importance of scalable, accurate, and real-time diagnostic mechanisms for hardware quality assurance (QA). Traditional factory QA processes are manual, time-consuming, and lack adaptability to subtle performance degradation. This study proposes an automated diagnostic pipeline that leverages publicly available GPU telemetry-like data, including hashrate, power draw, and efficiency metrics, to simulate factory-grade fault detection. Using the Kaggle “GPU Performance and Hashrate” dataset, we implement a machine learning-based framework combining XGBoost for anomaly classification and Long Short-Term Memory (LSTM) neural networks for temporal efficiency forecasting. Anomalies are heuristically labeled by identifying GPUs in the bottom 10% of the efficiency distribution, simulating fault flags. The XGBoost model achieves perfect accuracy on the test set with full interpretability via SHAP values, while the LSTM model captures degradation trends with low training loss and forecast visualizations. The framework is implemented in Google Colab to ensure accessibility and reproducibility. Diagnostic outputs include efficiency analysis, prediction overlays, and automated GPU health reports. Comparative results show higher efficiency variance in GeForce GPUs versus the more stable performance of data center models, highlighting hardware class differences. While limitations exist, such as reliance on simulated labels and static time windows, the study demonstrates the feasibility of ML-driven, scalable diagnostics using real-world data. This approach has direct applications in early fault detection, GPU fleet management, and embedded QA systems in both production and deployment environments.

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