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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 79 Documents
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Search results for , issue "Vol 5, No 2 (2025)" : 79 Documents clear
Quantum Machine Learning for Enhancing Signal Processing Applications Sairam, Kamineni; Deepak, Shashikant; Chakravarthi, Rekha; Mohanty, Saumendra Ku.; Rao, P.S. Raghavendra; Choudhary, Varsha; Punia, Ankit
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.v5i2.1375

Abstract

In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Predictive Data Analytics for Fault Diagnosis and Energy Optimization in Industrial IoT Environments Fallah, Dina; Abdul-Kareem, Bushra Jabbar; Murad, Nada Mohammed; Mahdi, Ammar Falih; Janan, Ola; 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.v5i2.1392

Abstract

The fusion of predictive maintenance with energy optimization represents a critical advance for intelligent Industrial Internet of Things (IIoT) systems. In response to the growing industrial demand for highly reliable and efficient operations, this study introduces and validates a unified framework that couples fault diagnosis via deep learning with energy management via reinforcement learning. We utilize a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for multivariate fault detection, which demonstrates superior classification accuracy and robustness against data incompleteness. Simultaneously, a Deep Q-Network (DQN) performs dynamic energy scheduling based on predicted system health, achieving substantial energy reductions without compromising task deadlines. Extensive experimental results from real-world industrial datasets and simulations confirm the integrated framework's superiority over conventional approaches in both diagnostic precision and energy efficiency. Key performance indicators, including inference speed and cross-validation, affirm its suitability for real-time industrial applications. This work demonstrates that integrating predictive analytics into intelligent control paradigms is crucial for improving the reliability and sustainability of modern IIoT systems and offers a replicable blueprint for developing next-generation smart manufacturing solutions.
Engineering Waste Management Systems: Efficiency Through Strategic Planning and Management Tools Ugli, Isroilov Sardorbek Solijon; Ghate, Atul Dattatraya
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.v5i2.1385

Abstract

Expanded life expectancy and higher health consciousness have been the pivotal contributors to the growth of the medical service sector. With a growing population, medical service facilities have additionally increased largely and proportionately the quantity of BMW produced. The growing quantity of biomedical waste is a public health issue internationally, drawing concern from all health authorities, associations and the government. The current biomedical waste management system in India has flaws that lead to inefficient and unproductive biomedical waste treatment procedures.  A thorough understanding of the types and quantities of waste that must be managed is necessary for the first crucial stage in the biomedical waste management system, which is creating a trustworthy waste management plan.  Therefore, the current study considered both identifying constraints related to biomedical waste management in hospitals and situational evaluation of biomedical waste management techniques.  By using interpretive structural modelling and environmental auditing techniques, this study seeks to assess current biomedical waste management procedures and investigate several obstacles that stand in the way of an efficient and successful biomedical waste management system.
Measuring the Level of Security Awareness of Smartphone Users Among Universitas Malikussaleh Students Using the Fuzzy Analytical Hierarchy Process Method Andreansyah, Sabda; Ula, Munirul; Afrillia, Yesy
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.v5i2.861

Abstract

Technology is developing rapidly; its benefits are manifold. The development of technology, especially smartphones, Has become a part of everyday life that cannot be distinguished anymore. The increasing number of smartphone users has also impacted the rising information security and privacy cases caused by a lack of awareness of spam, malware, and phishing. Many users upload personal information such as photos, phone numbers, and addresses without antivirus protection. This study aims to identify security and privacy challenges in smartphone use by measuring problems in the dimensions of attitude (knowledge), knowledge (attitude), and behavior (behavior). There are five focus areas: Backdoor, hardware, and AndroidOS, which is still low compared to applications and permissions. The method used the Analytical Hierarchy Process (AHP) with the Fuzzy concept to measure the level of information security awareness of Malikussaleh University students who use Android phones. The results showed that the overall level of understanding was good (80%). Although the attitude and behavior dimensions showed good awareness, the knowledge dimension was moderate. This may be why information security breaches still often occur among Android phone users. Faculty of Economics, Less Aware: 23 people Unaware: 1 person. Faculty of Social and Political Sciences, Less Aware: 24 people. Faculty of Teacher Training and Education, Less Aware: 21 people. Faculty of Law, Less Aware: 24 people. Faculty of Medicine, Less Aware: 27 people and Aware: 3 people. Faculty of Agriculture, Less Aware: 30 people. Faculty of Informatics Engineering, Less Aware: 70 people and Aware: 5 people. Total Awareness, Less Aware: 199 people, nine people, and Unaware: 1 person.
AI-Driven Self-assembled Nanophotonic Crystals for High-performance Optical Computing Kushwaha, Ragini; Raja, Adil; Pant, Seema
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.v5i2.1363

Abstract

A nanophotonic crystal (NPEC), however, can represent a transformative approach for AI-based optical computing and ultimately computing at the nanoscale, replacing individual components with the self-assembly of an infinite array of nanostructures on a chip with one vision foreseen as ultra-fast, energy-efficient artificial photonic neural network. Finally, this research proposes an innovative framework in an AI-driven self-assembly technique to create defect-free nanophotonic structures based on the tunable optical properties. Through the integration of AI-guided dynamic reconfiguration mechanisms, these crystals can dynamically reconfigure light propagation paths in real time and therefore boost very high computational speed and minimise power consumption. Based on the idea of AI-assisted refractive index tuning and programmable optical waveguides, the proposed system can be used to implement logic gates and deep learning operations, using which we can avoid traditional electronic computing. We experimentally validate the feasibility of this approach using matrix multiplications and a convolutional neural network (CNN) acceleration running up to 87-fold faster than comparable conventional silicon-based architectures. Furthermore, the self-assembled nanophotonic processors are integrated with quantum photonic systems for neuro-morphic computing of the near future. They address key challenges specific to photonic computing, and this advances the frontier of photonic computing in the areas of both fabricability, scalability and defect control, and energy efficiency. This suggests that optical AI hardware using AI-driven self-assembled nanophotonic crystals can significantly improve the operation of optical AI hardware, from very efficient and fast computing solutions for machine learning, data analytics, and AI-enhanced applications.
Advanced Power Electronics for Grid-Integrated Renewable Energy Systems Poonguzhali, S.; Mohapatra, Tapas Kumar; Boregowda, Vinay Kumar Sadolalu; Thakur, Ankita; Bhalla, Anubhav; Sairam, Kothakonda; Deepak, Shashikant
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.v5i2.1387

Abstract

Incorporating Renewable Energy (RE) into the grid necessitates efficient and dependable power conversion phases, especially given the rising need for enhanced controllability and adaptability from the system. Powered by sophisticated control and data technologies, power electronics converters are crucial for large-scale RE production. The deployment of power converters has revealed several issues within traditional power grids, such as decreased system stability. The paper presents grid integration with power electronics for large-scale RE production. Technical challenges and specifications are examined, emphasising grid-dependent wind, solar power, and energy storage solutions. The fundamentals of energy production and converting control for individual electrical conversions (e.g., generalised current management) and at the system layer (e.g., coordinated functioning of large-scale energy networks) are briefly addressed. Further studies are proposed to enhance large-scale RE generating capabilities by integrating additional power electronics technologies.
A Comparative Study of Data Mining Models using Essential Metrics in the Prediction of the Relation Between Polycystic Ovary Syndrome and Postpartum Depression in Women P. Pillai, Arya; Chinnasamy, N.V.
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.v5i2.1364

Abstract

Around the world today, personal health care is an unavoidable task to be done in human life. The vest emergence of medical science and medical technology is accelerating day by day. In many paradigms, information technology plays a vital role in comparing the past evidence with the present in the medical field and as a result, predictions will be outlined. Data mining and Data mining algorithms in medical care have a major role in improving personal care and in the overall healthcare system. Women with PCOS are more reasonably experience several pregnancy issues, including diabetes mellitus, hypertension, anxiety and mood swings, which may sometimes lead to Postpartum Depression. This paper evaluates a few parameters related to health care and predicts the relationship between PCOS and PPD in women based on data mining approaches.
Optimisation of Resource Allocation in Large-Scale Engineering Projects Using AI-Based Decision Models Nuritdinovich, Muhidinov Ayubbek; Roy, Jainish
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.v5i2.1390

Abstract

In software development, varied decisions need to be made to ensure the fulfilment. Customers frequently seek a wide range of functions in large software projects, resulting in a vast set of requirements. Due to project timeframes and resource constraints, implementing all of the requirements is usually not possible. Setting priorities for a large number of requirements takes time and is challenging. As a result, an organised method of prioritising and subsequently choosing the primary set of needs based on several factors is required. Diverse techniques were available to prioritise the requirements effectively. But the accuracy and time consumption for Requirements Prioritisation were not optimised. Also, during the large-scale Requirements Prioritisation, multiple aspects such as time, cost are not considered. Therefore, three novel methods are proposed for enhancing the performance of large-scale Requirements Prioritisation with better accuracy and less time. Many resource plans were affected by the unexpected joining and leaving events of human resources, which may cause uncertainty. This uncertainty can also affect the quality of the project delivery. Appointing a developer to the first allotted task until the completion of the same may reduce the flexibility of human resources, even though the developer can do other tasks. Optimised Event-Based Scheduler handles this uncertainty and resource flexibility. It is pretty commonplace that we need more time for scheduling if the developer's record is enormous. Subsequently, the search space is also big, and in the long run, the resource allocation is not on time.
Evaluating the Quality of Agglomerative Hierarchical Clustering on Crime Data in Indonesia Rizkya, Dini Dara; Retno, Sujacka; Yunizar, Zara
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.v5i2.863

Abstract

This study evallualtes the quallity of ALgglomeraltive Hieralrchicall Clustering with single linkalge, complete linkalge, alveralge linkalge, alnd walrd linkalge on the daltalset of the number of criminall calses in Indonesial (20ll0ll0ll-20ll23). The analysis compares clustering performance on the original and normalized datasets using the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Callinski-Harabasz Index (CH). The results showed that Ward Linkage provided the best clustering results, with the highest CH increasing from 65.826 to 66.873, clear cluster separation, and a stable structure (NMI = 0.5855, ARI = 0.6298). Single Linkage experienced a chaining effect, although it showed improvement in DBI from 0l.1793 to 0l.1765 and SS from 0l.6271 to 0l.640l0l, with NMI and ARI stable at 0l.4537 and 0l.5865, but CH decreased from 21.731 to 21.0l72. Complete Linkage was too aggressive in separating the data, shown by an increase in DBI from 0.5327 to 0.7116 and a decrease in SS from 0.6336 to 0.5830, although CH increased from 64.244 to 66.873. Average Linkage showed stable results, with NMI = 0l.6481 and ARI = 0l.7993 remaining, but a slight decrease in DBI from 0l.3874 to 0l.40l91, SS from 0l.6839 to 0l.6825, and CH from 42.358 to 40l.251. Data normalization generally helps to improve clustering quality by reducing the influence of feature scale differences. Several metrics showed improved cluster separation on normalized data, although the impact varied depending on the linkage method. Overall, Ward Linkage with normalization is recommended as the best method to produce more accurate clustering in Indonesia's crime data analysis. 
Physiological Characteristics of Weeds in Organic and Conventional Arabica Coffee Plantations in Bener Meriah Regency N, Mutiara; Badhawi, Badhawi; Hafifah, Hafifah; Nasruddin, Nasruddin; Yusra, Yusra
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.v5i2.1368

Abstract

This study aims to determine the diversity and differences of weed species found in organic and conventional Arabica coffee plantations in Bener Meriah Regency. Weeds are a limiting factor in crop productivity as they compete for nutrients, water, and sunlight. The research employed a survey method using 50 cm × 50 cm quadrants at four sampling points in each plantation type. Data were quantitatively analysed based on density, frequency, and dry weight, and Shannon-Wiener diversity and Sorensen similarity indices were calculated. The results identified 12 weed species, with Bidens pilosa L. as the most dominant. The diversity index was higher in organic plantations (H' = 1.8) than in conventional ones (H' = 1.4), while the weed species similarity index between the two systems was only 42%, indicating substantial differences. Weed chlorophyll content was also generally higher in organic plantations. These findings provide a scientific basis for developing targeted weed management strategies tailored to each plantation system.

Page 7 of 8 | Total Record : 79

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