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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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Multi Agent Model Analysis in Identifying the Relationship Between Productivity Level, Production Cost and Product Selling Price Kushariyadi, Kushariyadi; Sihombing, Dina Agnesia; Husaini, Husaini; Turyadi, Ito; Zakaria, Muhammad
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.514

Abstract

This study aims to analyze the remuneration strategy and the dynamics of a highly competitive market that affect employee productivity and satisfaction. This research method uses a quantitative approach combined with comparative statistics. Companies often choose a per-piece pay strategy to encourage efficiency and productivity while managing labor costs. However, companies may shift to a profit-sharing system to increase employee engagement and loyalty. Differentiation of payment schemes, such as performance-based incentives, plays an important role in attracting and retaining talent and aligning strategies with business goals, which in turn helps manage labor costs and reduce competitive pressures. Productivity and price sensitivity of demand also influence company strategy in markets with high productivity and low demand sensitivity, companies tend to adopt more innovative strategies, while in markets with low productivity and high costs, they often implement more homogeneous and conservative strategies. Further studies are necessary to explore additional factors such as differences in recruitment strategies, industry characteristics, and other variables that influence company decisions in remuneration strategies and labor distribution. Understanding these findings enables companies to design more effective remuneration strategies based on market conditions and their workforce requirements.
Machine Learning-Based Heart Failure Worsening Prediction Model to Build Self-Monitoring Prototype as an Effort to Prevent Readmissions and Maintain Quality of Life Rahardja, Untung; Hartomo, Kristoko Dwi; Sutedja, Indrajani; Kho, Ardi; Kamil, Muhammad Farhan
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.v5i1.1467

Abstract

Heart failure is a long-term condition of great concern which calls for health care services in cycles. This significantly hampers quality of life for patients and increases costs for the healthcare systems. If the worsening of heart failure could be detected early, the intervention to prevent readmission could be employed, such that readmission would be avoided, enhancing the quality of life for the patient. Accordingly, the paper explains how such a model to predict the worsening of heart failure in patients who are at high risk of this condition has been developed. The model uses information gathered from the Electronic Health Records (EHRs) (Clinical Variables, Vitals, Test Results, and Demographics) to make accurate predictions on patients. As an effective and efficient approach towards achieving this goal, comparison of different algorithms such as random forests, support vector machines and gradient boosting has been employed towards the building of the final model. At this stage, the model is embedded into a user-friendly self-monitoring device, allowing the chronic heart failure patients to assess health indices on the fly with the help of the mobile app and wearable devices. This secondary prevention strategy makes patients more responsible for their health and decreases the number of patients readmitted to the hospital by increasing their functioning and well-being. The paper further projects the future development of other forms of treatment for chronic heart failure, especially at the first line, focusing primarily on the timing and succession.
AI-Assisted Animation Storyboard Design and Automated Storyboard Generation Ou, Han
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.v5i2.1534

Abstract

This paper develops an Artificial Intelligence assisted animation storyboard design framework that uses Stable Diffusion 1.5 (SD-1.5) together with Visual Geometry Group 1-Convolutional Neural Network (VGG-1CNN) and Generative Pre-trained Transformer 3.5 (GPT-3.5) to produce automated game character images and narrative-focused storyboards. The proposed system utilizes combined text and sketch prompts for generating storyboard frames which preserve visual coherence together with stylistic continuity. The three main elements that power improved image generation through advanced diffusion control techniques include Contrastive Language-Image Pretraining (CLIP) neural networks and VGG-1CNN and Variational Autoencoder (VAE). The sequence starts by translating textual descriptions into numerical latent space codes using a neural network before the computer generates images based on these guidelines. The basic sketch receives edge detection through Canny edge maps to give better results in image refinement. By applying the VGGNet architecture to vector representations of generated images the system improves visual precision together with prompt compliance. The image quality receives additional enhancement through an iterative scheduler-based removal of noise which refines vector representations during multiple successive stages. The deployment of GPT-3.5 gives the system ability to create written narratives suited for each story frame while preserving narrational continuity. A decoder-based upscaling technique applies to the final output to generate high-resolution visually appealing storyboard frames that properly highlight the visual elements alongside textual content. The automated solution established through this model delivers an efficient pre-production animation pipeline automation that minimizes work efforts and conserves artistic and narrative quality.
AI-Assisted 3D-Printed Biomaterial Supercapacitors for Green Energy Storage Kadao, Anjali Krushna; Prashant, Patil Manisha; Sardana, Sunaina
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.v5i1.1361

Abstract

Advancements of biomaterial-based supercapacitors have been fuelled by the growing demand for sustainable and high-performance energy storage solutions. This work suggests the use of artificial intelligence to develop an AI-assisted 3D printed biomaterial supercapacitor, namely comprising electrode materials optimised by artificial intelligence (AI), bio-based electrolytes, and intelligent performance monitoring to increase efficiency and sustainability. It is an AI-driven approach that selects and optimises the biomaterials: high conductivity, low internal resistance, and excellent charge retention. Porous electrodes can be deliberately engineered on microscales by advanced 3D printing techniques; these perform well in facilitating fast ion diffusion and high energy storage capacity. This is achieved through experimental results of a 45% increase in capacitance, 68% reduction in charge transfer resistance, and 18% improvement in cycle stability on conventional supercapacitors. Moreover, AI-powered predictive maintenance increases the life of the device by 60%, thereby reducing unplanned failure by 60%. The involvement of biodegradable and non-toxic inclusion of materials encourages environmental sustainability, and thus, this supercapacitor is a green alternative for next-generation energy storage applications. This solution is suitable for wearable electronics, renewable energy systems, as well as smart devices, with high efficiency, low environmental impact and intelligent monitoring capability. The energy storage technology presents instances where AI, biomaterials, and 3D printers have the potential to transform the energy storage technology into a scalable, eco-friendly, and intelligent supercapacitor for future energy demands, according to this study.
Digital Detox and Mindfulness: Psychological Effects of Reducing Mobile App Usage Among University Students Norquziyeva, Zebo; Davlatova, Zebo; Kholnazarov, Umid; Edilboyev, Unarbek; Sattorova, Zilola; Xamrakulova, Kamola; Nurullayeva, Nodira; Shabbazova, Dilfuza
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.v5i2.1493

Abstract

In the contemporary digital era, college students are among the most frequent users of mobile applications and social media systems, raising substantial worries around addiction to technology. Although online resources provide many benefits, their improper usage and excessive reliance present significant risks. This research examines the impacts of a one-day Digital Detox (DD) program on undergraduates in Uzbekistan, concentrating on the consequences of refraining from smartphone use. The detox camp, modeled after similar programs, sought to assess the effects of a total DD. The research used qualitative approaches, conducting comprehensive conversations with respondents to evaluate improvements in self-awareness, connections with others, and general well-being. The results showed that people were more aware of themselves, had better connections, and felt much more relaxed. There were problems like Nomophobia (the fear of being without a mobile device) and FOMO (the fear of missing out).The findings demonstrate that DD programs significantly reduce digital reliance and promote conscious technology use among students. This study improves what we already know about the benefits of DD approaches and points out areas that need further research and application.
Algorithms and Modeling for Optimizing Sustainable Energy Systems Jaleel Maktoof, Mohammed Abdul; Shaker, Alhamza Abdulsatar; Nayef, Hamdi Abdullah; Taher, Nada Adnan; Yousif Al Hilfi, Thamer Kadum; 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.v5i1.1457

Abstract

The global transition toward sustainable energy necessitates intelligent, integrated solutions to overcome the intermittency of renewable sources. This paper presents and validates a comprehensive framework for optimising Hybrid Solar-Wind Energy (HSWE) systems by integrating advanced simulation, machine learning-based forecasting, and metaheuristic optimisation. Using meteorological and operational data from three distinct climate zones, we modelled and analysed a PV-wind-lithium-ion hybrid system. A neural network was employed for precise load forecasting, while Particle Swarm Optimisation (PSO) managed real-time resource allocation and storage dispatch. Comparative analysis reveals that the optimised hybrid system significantly outperforms standalone units, increasing energy production by up to 32%, improving overall energy efficiency to 92.3%, and reducing operational costs by over 36%. The simulation models demonstrated high fidelity, with predictions matching experimental field data with less than 1% error. Furthermore, the integration of predictive fault handling and intelligent load balancing enhanced system reliability, increasing the mean time between failures (MTBF) by over 70% and achieving 97.6% system availability. This research provides a validated, replicable framework for engineers and policymakers, demonstrating a practical pathway to developing efficient, economically viable, and resilient decentralised renewable energy infrastructure to meet global sustainability goals.
A Deep Learning-Based Pipeline for Feature Extraction and Segmentation of Endometriosis Stages: A Comparative Study of Transfer Learning and CDGAN Models Koshy, Soumya; Singh, K. Ranjith
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.v5i1.1351

Abstract

Top of Form This study proposes a deep learning-driven approach for extracting features and segmenting various stages of endometriosis from ultrasound images. The proposed pipeline integrates transfer learning with pretrained convolutional neural networks (CNNs) and conditional generative adversarial networks (CDGANs) to improve the accuracy and interpretability of the segmentation process. ResNet and DenseNet models are used in transfer learning to fine-tune pre-trained networks that classify the stages of endometriosis, and the performance of the model is improved by applying CDGAN on the dataset through data augmentation. From the comparison, the CDGAN-based method is more accurate and easier to interpret than the transfer learning model, so it is the preferred method for automatic staging of endometriosis. The results show improved accuracy (90%) and a higher F1-score (0.88), with CDGAN delivering the best segmentation results even in the most complex examples. Automating this portion of medical imaging for endometriosis has the potential to result in more informed treatment choices.
Deep Learning-Enhanced Hybrid Recommender Systems for Dynamic E-Commerce Platforms Lin, Chentao; Latih, Rodziah
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.v5i4.1702

Abstract

The success of current e-commerce relies on exact and varied recommendations which understand user context to enhance both user satisfaction and engagement levels. This research creates a deep learning-enhanced hybrid recommender system (DL-EHRS), which represents a deep learning-enhanced combination of recommendation systems specifically designed to operate in dynamic e-commerce environments. The proposed model connects Neural Collaborative Filtering (NCF) to Collaborative Filtering (CF) while using Deep Neural Networks (DNNs) together with Content-Based Filtering (CBF) to tackle existing recommendation system shortcomings. The performance benchmark of the DL-EHRS resulted in superior results than baseline models during all evaluation assessments. The recommendations produced through this system achieved high-quality performance at 98.1% accuracy, along with 97.9% precision and 97.8% recall and 97.9% F1-score. The proposed algorithm showed better processing speed than CF, CBF, and NCF because it completed operations in 0.9 seconds on average while readying real-time applications. The fast and stable training process of the model with minimum residual error proved its learning efficiency and ability to generalise through error convergence analysis. The proposed system meets user needs through a combination of latent factor learning techniques, content similarity analysis, along temporal context examination in its recommendation process. The integrated framework shows broad compatibility in online shopping environments because it produces precise predictions and deals with sparse data while generating better interfaces for users.
Performance Evaluation of Machine Learning and Deep Learning for Rainfall Forecasting Soebroto, Arief Andy; Limantara, Lily Montarcih; Mahmudy, Wayan Firdaus; Sholichin, Moh.; Hidayat, Nurul; Kharisma, Agi Putra
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.v5i4.1179

Abstract

Climate change is a significant challenge for both humans and the environment, with its impacts increasingly felt across various regions of the world. The most evident consequence is the alteration of extreme weather patterns, which often lead to destructive and life-threatening natural disasters. Among these, extreme rainfall was the most damaging factor, frequently triggering floods. However, the increasing occurrence of related events outlined the urgent need for developing more accurate rainfall forecasting systems as a strategic measure for disaster risk reduction. This research adopted daily rainfall data from Samarinda City, collected between 2004 and 2012, to conduct prediction using both machine and deep learning methods. The implementation of machine learning methods, such as Support Vector Regression (SVR), enabled the model to learn from historical data and uncover complex patterns, resulting in accurate forecasts and improved adaptability to climate variability. Meanwhile, deep learning models, including Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), enhanced prediction performance by capturing more intricate and abstract data relationships. Performance evaluations conducted using Mean Absolute Error (MAE) and Mean Squared Error (MSE) showed that deep learning outperformed machine learning in accuracy. The LSTM model achieved the best performance, with loss values of 0.0482 and 0.0527 for MSE and MAE, respectively. The advantage of deep learning lies in its ability to build more complex models for handling non-linear problems and to learn data representations at various levels of abstraction, which has led to more accurate results. Furthermore, LSTM surpassed RNN by effectively overcoming the vanishing gradient issue, allowing for more stable and efficient training that led to superior predictive performance.
A Blockchain-Based Framework for Secure and Interoperable Healthcare Data Management: An Empirical Study Deng, Zilong; Alobaedy, Mustafa Muwafak; Hafiz, Mohd Nurul; Huang, Xiaocun
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.v5i4.1701

Abstract

The digitisation of healthcare has resulted in a greater dependence on Electronic Health Records (EHRs), yet traditional centralised systems encounter ongoing difficulties with data security, interoperability, and adherence to regulations. This research presents a blockchain-oriented framework, created with Hyperledger Fabric, to tackle these constraints. Utilising a mixed-methods strategy, we assess the system's performance under normal, peak, and stress scenarios by employing one million synthetic EHR transactions. Essential metrics comprise transaction latency (2.3s), throughput (1,150 TPS), data integrity (100%), and effectiveness of access control. The results show a 30% reduction in data management errors and overall data retention. A comparative evaluation against traditional systems confirms blockchain's superior resilience and privacy safeguards. However, scalability constraints were observed during peak loads, highlighting the need for Layer-2 improvements and hybrid architectures. This research offers empirical proof validating the viability of blockchain for the secure, scalable, and regulation-compliant management of healthcare information. 

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