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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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Machine Learning-guided Synthesis of Quantum Entangled Materials Vij, Priya; Nandy, Manish; Pandey, Mamta
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.v5i2.1496

Abstract

The synthesis of materials into quantum entangled materials is a complicated challenge to an accurate and computational prediction of those materials. In this proposed work, it develops an AI-guided framework based on the combination between machine learning (ML) and reinforcement learning (RL), and quantum simulations to push the designing and validating of quantum materials at a much faster pace. In the first, graph neural networks (GNNs) are used to extract the atomic level quantum features, and in the second, generative models (VAE/GAN) are utilized to discover some novel entangled structures. In addition, fabrication with the synthesis parameters as parameters in the reinforcement learning results in an improvement of the experiment synthesis and a decrease of experiment failures as well as significant improvement of reproducibility. It demonstrates that the proposed hybrid ML-quantum simulation is validated on entanglement fidelity in real-world quantum computing platforms using IBM Qiskit and Google Cirq. As the proposed method is way beyond traditional ones, it has higher quantum coherence time, synthesis efficiency as well as higher prediction accuracy. In addition to enabling scaling-up of cryptography, quantum computing, and next generation nanomaterials, it is a cost and scalable framework for creating next generation quantum technologies applications as it is. And the model is further researched for the generalization in regards to real-time experimental feedback and for the expansion of the framework to a more general quantum materials program. The results show that AI approaches can truly accelerate the quantum material innovation even when syntheses are not at all possible.
Safety Function Model for Requirement Specification in Critical Systems: A Case Study of Generic Patient Controlled Analgesia Pump Model (CGPA) Abdullah, Azma; Abu Bakar, Rohani; Abdul Farid, Fairus; Abdulhak, Mansoor
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.v5i3.1370

Abstract

Developing safety-critical systems (SCS) involves a systematic method for assuring and providing safety and dependability. Conventional approaches rely on expert intervention, which can introduce bias, cause delays, and promote inconsistency. This work proposes a model that enhances efficiency and accuracy by extracting safety functions from requirements specifications. The model is made up of three main steps: (1) preprocessing, which involves getting rid of stop words; (2) string selection and matching using a database of safety properties variables based on literature and expert knowledge; and (3) putting safety and non-safety functions into a structured safety function log. The model was trained and tested with the CGPA insulin pump and got a 94% F1 measure score, which means it was 91% accurate, 96% accurate, 92% precise, and 96% recall. This shows that it is good at making things clearer and less biased when finding functions for safety against failures, malfunctions, operational hazards, and inconsistencies in safety-critical specifications. All these enhancements contribute towards Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities, aiming to develop safer, resilient, and sustainable infrastructure in safety-critical regions.
UX Matters: Unlocking QRIS Adoption among MSMEs in the Greater Jakarta Area Ramadhan, Muhammad Daffa; Fajar, Ahmad Nurul
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.v5i4.1337

Abstract

This study investigates the influence of User Experience (UX) dimensions, integrated with the Technology Acceptance Model (TAM), on the adoption intention of Micro, Small, and Medium Enterprises (MSMEs) in the Greater Jakarta area toward the Quick Response Code Indonesian Standard (QRIS). The research examines functional qualities, which consist of Efficiency, Perspicuity, and Dependability, alongside hedonic qualities, represented by Stimulation and Novelty, as well as Trust, which serves as an essential construct in the adoption process of financial technologies. These factors were evaluated as direct predictors of adoption behaviour, while Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) were employed as mediating variables to capture the mechanisms underlying the relationships, consistent with TAM’s theoretical framework. Data were collected from 400 MSMEs across various industries in the region, and analysis was conducted using Partial Least Squares–Structural Equation Modelling (PLS-SEM). The empirical results demonstrate that Efficiency strongly drives PU, emphasising the critical role of task performance and functional reliability in shaping perceptions of usefulness. Dependability and Trust significantly improve PEOU, highlighting that stable system performance and confidence in technology providers reduce complexity and foster ease of use. Interestingly, while Stimulation shows a positive and direct impact on Intention to Use, Perspicuity and Novelty yield unexpected negative effects, suggesting that overly simple or overly unfamiliar experiences may hinder rather than encourage adoption. Furthermore, PU and PEOU are shown to mediate several causal paths, reinforcing TAM’s theoretical assumptions and underscoring the value of integrating UX considerations into classical acceptance models. The final structural model exhibits strong explanatory power, with an R² of 0.903 for Intention to Use, indicating the robustness of the integrated framework and confirming the effectiveness of combining UX dimensions with TAM in explaining QRIS adoption behaviour among MSMEs.
Federated Learning Architectures for Privacy-Preserving Smart Grid Data Processing Abdulkareem, Sarah Ali; M. Kallow, Sabah; Bako, Imad Matti; Abdullah, Salima Baji; T.Y. Alfalahi, Saad; Batumalay, M.
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.v5i3.1423

Abstract

The use of smart data in smart grid infrastructure has lately become essential for efficient power distribution, instantaneous?decision-making and overall system protection. Nonetheless, the application of centralized machine-learned models is impeded by?privacy issues, nonhomogeneous distributed data sources, and communication constraints. In this paper, we propose a federated learning framework to handle these challenges and support decentralized, privacy-preserving?model training across a wide range of smart grid components such as residential meters, substations, and electric vehicle charging stations. The proposed method develops a multi-staged framework, which includes adaptive differential privacy, gradient compression, and topology-aware aggregation to improve?the model's performance in the meanwhile of data privacy. The robustness of the system is demonstrated by energy profiling, cross-domain generalization test and temporal?stability analysis. Findings indicate the model has good prediction performance across different grid setups and customer profiles and that energy use and privacy?noise are within acceptable limits for operational use. Furthermore, the architecture shows?strong generalization to unseen domains, and robust performance through many federated training rounds. By considering?computational efficiency, privacy limitations and topological heterogeneity, this work provides a scalable and secure real-time energy intelligence approach. Results suggest that federated?learning with adaptations to the smart grid is a promising approach for robust privacy-preserving analytics applied to critical infrastructures. This work will support energy efficiency in the future which will be a process innovation. 
Gamification Design in Assisting Master's Students in Learning Readiness of XYZ University Stanley Salim, Sebastian; Wang, Gunawan
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.v5i3.974

Abstract

This paper discusses how to design and assess a gamification framework to increase the learning preparedness of postgraduate students at XYZ University. Academic success at the master's level largely depends on learning readiness, and most students have to face numerous challenges, including poor time management, thesis composition, a lack of motivation, and insufficient social interaction with peers. Such obstacles inevitably result in low productivity and low engagement, which eventually influence the quality of their academic performance. In order to solve these problems, the concept of gamification is proposed as a new approach to pedagogy that incorporates the elements of a game into the learning process. The system included features like experience points (XP), leagues, leaderboards, and guided challenges to make the system more motivational, maintain engagement, and collaborate with students. The quantitative research design was chosen, and approximately 50 respondents who are enrolled in the program and participated in the gamified learning activities were used in the study. The results prove that gamification is a powerful tool that promotes learning preparedness by motivating success through reward systems and an opportunity to interact with peers through group activities and online discussion forums. The students claimed to be more motivated, better concentrated on the milestones of the thesis, and more disciplined in managing their time than they are using the traditional approaches. In addition, the system provides a more organised, interactive, and fun learning experience that allows participants to resolve academic difficulties more successfully. According to the assessment, the research indicates that gamification is an emerging tool that can be used to increase postgraduate learning preparedness. It suggests additional design improvements to the user interface, personalisation, and differentiation of reward systems to ensure the highest student engagement and effectiveness in the long term.
Integrating Cloud Storage in STEM Education: A Case Study on Collaborative Project-Based Learning Imomova, Umida; Tleuzhanova, Manatzhan; Sattorova, Zilola; Khaydarova, Mahliyo; Doniyarov, Mavlonbek; Nasritdinova, Umida; Saidov, Madilkhan
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.v5i1.1360

Abstract

When science, technology, engineering, and mathematics (STEM) are combined, these topics provide children with the knowledge and skills they need to become intelligent, responsible adults. The primary teaching approach used in this research was Project-Oriented Problem-Based Learning (Po-PBL), which examined the effects of an integrated STEM education system on students' 21st-century competencies. A one-group quasi-experimental methodology and polling techniques were used to assess the students' understanding before and after the program began. The findings demonstrated that pupils' overall 21st-century abilities significantly improved. This was particularly true for their production skills, which improved from mediocre to excellent. Because Po-PbL requires students to focus on real-life issues and discover answers, it is evident that it is particularly beneficial for students in STEM areas. The research emphasizes the value of incorporating Po-PbL into STEM education to assist students in improving their problem-solving, creativity, teamwork, and communication skills. When students work on projects with their hands, they use what they already know and discover new things. These abilities will help students deal with a challenging situation in the future.
Artificial Intelligence in Film and Television Production: Idea Generation and Post-Production Li, Kang
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.v5i2.1531

Abstract

This paper takes the impact of artificial intelligence on film and television art creation as the topic, from the creative generation of film and television art creation and post-production, two aspects of the impact of artificial intelligence on film and television production. The impact of artificial intelligence on film and television production for a more in-depth discussion and research, combined with examples of research and analysis, the use of science and technology point of view theory of film and television art creation in the new era of the artistic impact of the presentation of a specific description. Through the study, artificial intelligence plays a pivotal role in the process of film and television production, from pre-planning to script writing to later video editing and special effects production. The successful use of artificial intelligence in the field of film and television art creation has a great impact on the overall value chain involving the film and television industry, which is of great social significance.
Quantum AI-Enhanced Nanomagnetic Sensors for Biomedical Imaging Biswas, Debarghya; Balkrishna, Sutar Manisha; Aggarwal, Rashi
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.v5i1.1451

Abstract

An extremely high impact advance in biomedical imaging is quantum AI-enhanced nanomagnetic sensors, where the combination of quantum coherence and nano automotive AI provides ? substantial increase in medical diagnosis precision. This research outlines the QAI-NMS System that utilises quantum dots and nitrogen vacancy (NV) centres in diamond to improve the bio-magnetic sensing capability to sub-picoTesla sensitivity. The AI-driven quantum noise suppression and Quantum Classical Computing are hybrid, and both augment the signal clarity and reduce the quantum decoherence of the signal. The system uses real-time signal optimisation based on deep reinforcement learning, as well as high-fidelity biomedical imaging by the variational quantum algorithms. The conventional methods like MRI and CT are much invasive, radiated, and portable imaging techniques with less sensitivity, but QAI NMS is non-invasive, radiation-free, and portable imaging with higher sensitivity. Other can be developed, such as early cancer detection, neural activity mapping of the brain for a brain computer interface, non-invasive cardiac monitoring, and even to track drug delivery to a given area without actually interfering with the body. A quantitative analysis is provided for signal-to-noise ratio, quantum-assisted resolution enhancement, as well as computational efficiency, and experimental evaluations are presented that exhibit significantly improved signal-to-noise ratio. This study constitutes a paradigm shift in biomedical imaging by merging quantum technologies with AI analytics for realising real-time high-resolution noise-immune imaging. The proposed framework here would have a great application in the next generation of diagnostic tools, offering unparalleled precision in health monitoring as well as medical imaging. The future research will miniaturise, deploy, and augment what appeared quantum in nature to provide the capability for real-time clinical deployment.
Optimizing Supply Chain Logistics with Predictive Analytics: Using Data Science to Improve Cost Efficiency and Operational Performance Mehta, Rushabh
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.v5i3.1078

Abstract

Traditional reactive approaches to supply chain logistics are inadequate because global supply chains are consistently confronted with demand volatility, geopolitical risks and operation inefficiencies. This paper examines how predictive analytics, a fundamental field in data science, can be applied to streamline logistics to make operations not only more cost-efficient but more efficient. The study utilizes machine learning algorithms, time-series forecasting, optimization models, and simulation toolsets to implement a mixed methodology based on literature synthesis, case analysis, and model evaluation on the most important logistics functions. The secondary sources such as industry reports, peer-reviewed articles, and validated case studies were used as a source of data. The results show that predictive analytics produce quantifiable benefits in various areas. Machine learning adoption in demand forecasting and inventory optimization in companies like Amazon and Walmart cut stockouts to less than 5% and lower the number of overstocks by 2050 to up to 25% inventory holding costs. Optimization in transportation: DHL announced that through dynamic route optimization based on AI models, fuel expenses were cut by 15% and delivery times in cities were shortened by 12 percent. Predictive modeling ensured a greater efficiency of the warehouse and resulted in a 15-percent decrease in the variability of order processing and labor allocation optimization. By identifying supplier delays, quality risks and geopolitical threats proactively, risk management applications posted a 45.3 percent reduction in supply chain disruption. Further, the predictive variance analysis delivered 10 percent procurement cost savings to a firm like Nestle, demonstrating the advantages of supplier performance. This study concludes that predictive analytics promotes an active, robust and cost effective supply chain. Predictive analytics is a groundbreaking direction toward the creation of agile logistics systems oriented to Industry 4.0 requirements despite the difficulties in data integration, technical complexity, and upfront costs.
Harnessing Backflow: AI-Optimized Hybrid Fan Systems for Micro-Scale Energy Regeneration and Smart Efficiency Control Kumar V, Bhuvana; Yedukondalu, N.; Rao Appini, Narayana; Suresh Babu, Siddabathuni; Sreenu, Karnam
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.v5i3.1411

Abstract

The interest in sustainable energy applications is driven by the desire to improve hybrid systems that can consume and simultaneously recover energy in a closed-loop situation. This research examines the possibility of an AI-based, self-reproducing fan that can recover and convert some of its own generated airflow, and convert that to usable electrical energy. Electric fans are inherently bound by their architecture to use their entire input energy for ventilation with no feedback for energy. However, the system here proposes a new fully integrated energy regeneration system by utilizing miniaturized axial turbines, or piezoelectrics, placed within the momentum of the airflow to utilize any remaining kinetic energy as usable electrical energy. The proposed research study utilizes deep reinforcement learning (DRL) and multi-objective approaches based on evolutionary algorithms (MOEA). The proposed DRL and MOEA utilize adaptable meta-level optimization and real-time optimization of its geometric arrangement and turbine geometric arrangement and energy routing. The study's computational fluid dynamics (CFD) models will be validated by utilizing AI-supported simulation environments, iterates through the design space for the various configurations that optimize net energy and axial turbine efficiency without sacrificing their airflow efficiency, and use exhaust volumetric flow rates from the CFD. Energy recovery ratio, effect on fan impact and system sustainability index will be the indicators of success to evaluate the study's sustainable and energy-efficient application. This research takes a significant step in the area of micro-scale regenerative energy systems and suggests an intelligent control system that can respond to changing usage conditions. The implications provide significant opportunities that support developing next-generation smart fans, autonomous operation ventilation systems, and low-power AIoT (Artificial Intelligence of Things) devices. This research is a significant first step in trying to re-engineer airflow systems not as passive consumers of energy, but as active participants in energy recycling, that can contribute to drive innovation for green engineering and intelligent systems.

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