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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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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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Enhancing Healthcare Data Security and Integrity through Blockchain Technology in Hospital Information Systems Ming, Hu; Bin Ariffin, Shamsul Arrieya
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.v5i4.1715

Abstract

The goal of this research is to develop and test a blockchain-based framework whose target is to increase the level of security of, integrity , and operational efficiency in hospital information systems (HIS). As the increasing number of sensitive healthcare data and the increasing threats to data privacy, the following system integrates AES encryption, multi-factor authentication (MFA), role-based access control (RBAC), blockchain storage and smart contracts to ensure secure and transparent data management. The methodology was that the framework was implemented in a simulated environment using the Hyperledger Fabric v2.2 on Ubuntu 20.04, and performance was measured for various metrics such as encryption time, transaction time, storage efficiency, scalability, and system responsiveness. Comparative analysis was done to evaluate the user related metrics like ease of use, adoption rate and user satisfaction against the benchmarked metrics reported in the previous body of literature. The results indicate that the blockchain-based system is better than the traditional cloud-based and distributed systems, offering 80% of speeds of transactions, 90% storage efficacy, 85% of scaling, 95% of security, 90% of ease of use, and 80% of adoption. These results show the potentiality of blockchain in improving reliability, auditability, and trustworthiness of information systems in healthcare.
Computer Vision for Monitoring Renewable Energy Infrastructure Hussein, Ahmed Ali; Alal, Sumaia Ali; Abdulrahman, Saad Abdulaziz; Merzah, Hanaa Hameed; Abbas, Hasan Ali; Batumalay, M.
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.v5i1.1727

Abstract

The operational efficiency of renewable energy installations, including solar, wind, and hydropower systems, is often hindered by the limitations of manual inspections and legacy monitoring. These methods lack the real-time, scalable fault detection necessary to prevent costly downtime. This paper proposes a comprehensive computer vision framework for automated fault detection, predictive maintenance, and inspection optimization across diverse renewable energy infrastructures. We developed a hybrid deep learning model, based on ResNet-50 with attention-based extensions, to analyze high-resolution imagery from drones and stationary cameras. The model was trained and validated on a dataset of 20,000 labeled images covering infrastructure-specific defects such as photovoltaic microcracks, wind turbine blade erosion, and hydropower sedimentation patterns. Our experiments demonstrate high-performance, with fault detection accuracy exceeding 91% for all categories and inference latencies under 70ms. The system significantly improved predictive maintenance outcomes, reducing unplanned outages by over 77% and decreasing inspection energy consumption by more than 70%. Scalability tests on a larger 50,000-image dataset confirmed the framework's robustness, maintaining high accuracy and processing speed. This work validates computer vision as a viable, cost-effective, and scalable solution for intelligent monitoring in the renewable energy sector, offering significant practical implications for autonomous diagnostic systems in smart grid and industrial applications for energy efficiency.
Towards Intelligent Performance Monitoring for Blockchain-Based Learning Systems: A Multi-Class Classification Approach Sulaksono, Aditya Galih; Patmanthara, Syaad; Rosyid, Harits Ar
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.v5i4.1138

Abstract

This study proposes a multi-class classification framework for monitoring blockchain system performance as a step toward integration within blockchain-based learning management systems (LMS). Reliable performance monitoring is essential because smart contracts in educational settings depend on timely and accurate system responses to ensure valid grading and credential issuance. A dataset of 3,081 transactional logs was generated from simulated blockchain testbed, capturing throughput, latency, block size, and send rate. Throughput values were discretized into seven qualitative categories ranging from “Very Poor” to “Very Good” using quantile-based binning. Preprocessing involved data cleaning, categorical encoding, Z-score normalization, and label encoding to ensure model compatibility. Five algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were trained and evaluated using stratified 80–20 partitioning and 5-fold cross-validation with grid search for hyperparameter tuning. Performance metrics included accuracy, macro precision, recall, and F1-score. Random Forest achieved the best results with 91.35% accuracy, 0.910 macro precision, 0.911 recall, and 0.910 F1-score, outperforming other models by handling complex feature interactions and reducing variance. Decision Tree offered strong interpretability (88.32% accuracy), while Logistic Regression (84.97%) and SVM (84.86%) provided stable performance. KNN showed balanced results (87.78%) but incurred high computational costs. The findings demonstrate that multi-class stratification provides more actionable insights than binary methods, supporting low-latency decision-making for smart contract execution in decentralized LMS ecosystems. The novelty of this research lies in applying multi-class classification instead of binary methods, enabling nuanced monitoring. Future work will validate the framework in real blockchain-LMS deployments.
Security Challenges and Countermeasures in Next-Generation Wireless Networks Manjunath, Honganur Raju; Yadav, Aditya; Sarpal, Sumeet Singh; Mounika, Nagireddy; Patil, Shashikant; Nandhitha, N.M.; Behera, Santosh Ku.
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.v5i2.1492

Abstract

The prospects of new-age wireless networks, such as 5G and 6G technologies, include unrivalled speed, connectivity, and integration of multifarious devices. However, their sophisticated architecture— which encompasses network slicing, edge computing, massive IoT, and software-defined networking—creates even greater challenges in network security. These vulnerabilities could be exploited to launch advanced attacks such as Distributed Denial of Service (DDoS) attacks, eavesdropping, spoofing, or data manipulation. This study aims to explore the evolving threat landscape for next-generation wireless networks while reviewing both existing and emerging strategic solutions. Some of the counter-surveillance technologies incorporated include AI-based Intrusion Detection Systems, Trust Frameworks on Blockchain, strong cryptography, and credential-less authentication. Addressing the challenge of dynamic threat capabilities paired with innovative defences allows this research to propose concepts for the next resilient and secure wireless communication systems.
Context-Aware Systems for Proactive Energy Efficiency Services Hameed, Maan; Noori, Nabaa Ahmed; Suleiman, Aghaid Khudr; Abu-AlShaeer, Mahmood Jawad; Sabah, Ahmed; Batumalay, M.
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.v5i1.1728

Abstract

Static energy control systems are increasingly unable to meet the demands of modern built environments, where dynamic occupancy and fluctuating conditions lead to significant inefficiencies. This paper presents a context-aware system for proactive energy management that integrates real-time data acquisition, machine learning-based forecasting, and autonomous control. A multi-tiered architecture was developed and deployed across diverse settings residential, commercial, and industrial—to gather contextual data on temperature, occupancy, lighting, and equipment usage. The system uses predictive forecasting to anticipate short-term energy needs and reinforcement learning to optimize control strategies, ensuring both energy savings and user comfort. Results from the deployment demonstrate significant power reduction, high system responsiveness, and strong user satisfaction. Application-specific benchmarks revealed major efficiency gains in HVAC, lighting, and industrial machinery, while scalability tests confirmed stable performance under increasing sensor loads. This research validates the effectiveness of combining contextual intelligence with adaptive control to create sustainable, responsive, and human-centered energy systems. We provide a practical, modular framework for intelligent energy infrastructure in smart buildings and industrial parks. Future work will focus on enhancing model interpretability, integrating economic incentives, and exploring federated learning for distributed intelligence in support of energy efficiency.
An Improvement of License Plate Detection Under Low Light Condition Using CLAHE and Unsharp Masking Suleman, Fitriyanti; Indrabayu, Indrabayu; Mokobombang, Novy Nur R.A; Zulkarnain, Eliza; Fadhil, Muh. Wira Ramdhani
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.v5i4.1654

Abstract

The rapid increase in vehicle numbers has rendered traditional manual traffic monitoring approaches inefficient and unreliable, thereby emphasizing the need for intelligent, automated systems to assist in traffic management and law enforcement. Among these, Automatic License Plate Recognition (ALPR) systems play a crucial role in detecting and tracking vehicles. However, their performance often deteriorates under low-light or poor visibility conditions, leading to reduced detection accuracy. To address this limitation, this study proposes a two-stage image enhancement pipeline that integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) and Unsharp Masking (USM) techniques with the advanced YOLOv11 object detection model. The dataset utilized comprises 1,496 images extracted from Electronic Traffic Law Enforcement (ETLE) video footage captured in Makassar, Indonesia. These images were systematically divided into training, validation, and testing sets in a 70:20:10 ratio to ensure balanced model evaluation. Four experimental scenarios were conducted to assess the contribution of each enhancement method. The results revealed that the combined application of CLAHE and USM significantly improved detection accuracy, achieving a Precision of 0.945, Recall of 0.977, and a mean Average Precision (mAP@0.5:0.95) of 0.830—outperforming all other configurations. These findings confirm that the synergistic use of contrast enhancement and edge sharpening effectively mitigates the challenges posed by low-light environments. Consequently, the proposed approach enhances the robustness, clarity, and reliability of ALPR systems, offering a practical solution for real-world intelligent transportation applications and automated traffic law enforcement.
Data-Driven Cloud Systems for Renewable Energy Optimization Yousif, Hayder Abdulameer; Hussain, Salah Yehia; Hassan Ali, Taif Sami; Al-Doori, Vian S.; Sabah, Ahmed; Batumalay, M.
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.v5i2.1729

Abstract

The growing share of renewable generation in global power systems creates operational instability due to the volatile nature of solar, wind, and hydropower. This study presents a novel cloud-edge integrated model designed to enhance the performance and efficiency of these renewable sources through a data-centric approach. The proposed architecture relies on an IoT-enabled sensor network for real-time data gathering, processed through a hybrid infrastructure combining edge-level filtration with cloud-based analytics. For energy output prediction, we compared Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models, with LSTM demonstrating superior performance. To optimize operations, a multi-objective Genetic Algorithm was implemented to simultaneously minimize energy losses and costs while improving grid utilization balance. Furthermore, exergy-based modeling was employed to evaluate the thermodynamic quality of energy transformations. The results confirmed that the system significantly improved predictive accuracy, responsiveness, and energy savings. Under varying loads, the system maintained low latency and high energy allocation efficiency, validating its real-time adaptability. In summary, this research delivers a modular and scalable solution for intelligent energy management, highlighting the power of predictive analytics and adaptive control in creating data-driven, next-generation sustainable energy efficiency systems.
AI-Driven Text Analysis and Generation for Green Energy Applications Ahmed, Saif Saad; Mahdi, Mohammed Fadhil; Hammad, Qudama Khamis; Mahdi, Ammar Falih; Alfalahi, Saad.T.Y.; Maidin, Siti Sarah
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.v5i2.1745

Abstract

The rapid growth of the green energy sector has produced a massive volume of textual data, creating significant challenges for information extraction and decision support. This study investigates the application of state-of-the-art Natural Language Processing (NLP) models, specifically BERT and GPT-4, to automate and enhance policy drafting, market analysis, and academic research clustering. We evaluated these models on a corpus of over 200,000 energy-related documents, using a structured computational workflow to measure performance on semantic coherence, factual reliability, and processing efficiency. The results demonstrate substantial improvements over manual methods. The AI-driven approach reduced policy drafting time by 39% and error rates by over 58%, while increasing semantic alignment to 93.5%. In market report synthesis, the models improved topic extraction accuracy by over 10% and reduced summary generation time by 38%. For academic literature, thematic clustering accuracy reached 92.3%, with a 44% reduction in processing time. These findings validate that fine-tuned NLP models can serve as powerful analytical tools in the sustainable energy domain, enabling institutions to navigate complex regulatory and technical information more effectively. By providing a practical demonstration of how automated NLP solutions can augment human expertise, this work contributes to the applied use of AI in achieving global green energy objectives, while also considering the associated methodological and ethical implications.
Cloud Computing for Optimizing Sustainable Energy Networks Mwafaq, Lara; Meftin, Noor Kadhim; Rasheed, Ali Abdulameer; Al-Dulaimi, Mohammed K. H.; Hasan, Talib Kalefa; Batumalay, M.
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.v5i2.1730

Abstract

The increasing integration of renewable energy sources into power systems creates significant challenges for grid stability, efficiency, and scalability. This study investigates cloud computing as a strategic control layer for optimizing these sustainable energy networks. We designed and deployed a cloud-based energy management system that utilizes intelligent data processing, real-time load balancing, and predictive analytics to enhance the performance of decentralized grids. The methodology combines virtualized monitoring with adaptive fault detection and dynamic energy routing, allowing the system to respond autonomously to fluctuations in supply and demand. Our empirical evaluation demonstrates that cloud integration significantly improves transmission efficiency, reduces system downtime, and enables higher utilization of renewable energy, thereby lowering reliance on fossil-fuel backups. Key performance metrics, including data latency and machine learning inference time, were also enhanced, accelerating overall decision-making. These findings validate the hypothesis that cloud platforms are not merely computational tools but essential instruments for the global energy transition. The study concludes by discussing limitations related to cybersecurity and interoperability and proposes future research into hybrid cloud-edge architectures for energy efficiency.
Artificial Intelligence, Robotics, and Automation in Renewable Energy Systems Ismail, Laith S.; Faraj, Lydia Naseer; Mohammed, Doaa Thamer; Taher, Nada Adnan; Hafedh, Milad Abdullah; Maidin, Siti Sarah
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.v5i2.1746

Abstract

The transition to clean energy requires intelligent solutions to mitigate resource intermittency, grid instability, and operational inefficiencies. This paper presents and validates an integrated framework that leverages Artificial Intelligence (AI), robotics, and automation to optimize the performance and sustainability of renewable energy assets. The study employs machine learning models (LSTM, SVM, ANN) for energy forecasting, autonomous robotic platforms for real-time inspection, and advanced algorithms (MPC, Reinforcement Learning) for grid control. The framework's transparency and ethical compliance were validated using explainability techniques (SHAP, LIME) and cybersecurity protocols. Experimental results demonstrate significant performance gains across all domains. The AI models achieved high forecasting accuracy, with the LSTM model for wind power reaching a Mean Absolute Percentage Error (MAPE) of just 2.41%. Robotic inspections improved system uptime by nearly 30% and accelerated fault detection. In grid management simulations, a Reinforcement Learning-based control strategy proved most effective, reducing energy losses by 10.6% and control costs by 17.5%. This cross-disciplinary research illustrates the powerful synergy between intelligent software and advanced hardware in creating more reliable, efficient, and ethically grounded energy systems. The findings establish a scalable and validated foundation for next-generation renewable energy operations and highlight future pathways for enhancing human-machine collaboration in the pursuit of global sustainability targets.

Page 58 of 59 | Total Record : 582

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