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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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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 73 Documents
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Search results for , issue "Vol 5, No 4 (2025)" : 73 Documents clear
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. 
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.
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.
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.
Digital Transformation of Government: An Applied Science Approach to Smart Governance in Urban SDG Frameworks Alam, Syamsu; Syamsir, Andi; Rusdi, Rusdi; Sugiarto, Sugiarto; Bado, Basri
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.v5i4.1377

Abstract

This study applies an applied science approach to examine digital transformation in regional development, focusing on the Electronic-Based Government System (SPBE) and Smart Governance in Makassar City, Indonesia. The research evaluates the effectiveness, transparency, and contribution of technology-based governance systems toward achieving the Sustainable Development Goals (SDGs), particularly in strengthening urban governance performance. Employing a qualitative design that integrates Triangulation and the Logical Framework Analysis (LFA) method, this study investigates the practical impact of smart government initiatives and their correlation with development-oriented performance indicators. Findings reveal that insufficient interoperability of standards, protocols, and technologies impedes seamless data exchange across platforms, applications, and devices. This technical limitation has constrained Makassar’s smart governance outcomes, reflected in weak performance related to competitiveness, service efficiency, employment levels, and life expectancy. The study also identifies inadequate investment in smart infrastructure, research and development, and institutional capacity, which collectively hinder the realization of a data-driven, adaptive governance ecosystem. From an applied science perspective, this research underscores the necessity of strengthening interoperable digital architectures, ICT infrastructure, and human resource competencies to enhance system integration and decision support. By advancing engineering-based solutions for interoperability and data governance, regional governments can achieve higher levels of efficiency, transparency, and sustainability. The proposed framework provides a technological roadmap for aligning local smart city strategies with global development targets, demonstrating how applied digital innovations can bridge governance effectiveness and socio-development resilience in the context of sustainable regional transformation.
Exploring Servant Leadership and Work Commitment on Service Quality in Small and Medium Enterprises in the Agricultural Sector in Karawang, West Java Sutarman, Asep; Kadim, Ahmad; Hua, Chua Toh; Sihotang, Sondang Visiana
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.v5i4.1762

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play an important role in the national economy. MSMEs are a populist economic system that can help alleviate poverty, and their growth has the potential to expand the economic base and contribute significantly to improving the regional economy and national resilience. The large number of MSMEs can become government partners in resource management. The MSME development strategy focuses on the agricultural sector in Karawang Regency. The success or failure of government policies in advancing MSMEs in the agricultural sector in Karawang Regency depends heavily on leadership, work commitment, and service quality. No company can develop or operate well until MSME participants provide exceptional service quality, work commitment, and service leadership. Therefore, it is important to address service problems in the MSME sector, especially in the agricultural sector in Karawang Regency. The main objective of this study is to empirically investigate the impact of servant leadership and work dedication on service quality. Data were collected using an online survey distributed to 259 SMEs in the agricultural industry in Karawang Regency, Indonesia. Data were evaluated using SEM. Hypotheses were assessed directly and jointly using two-step Partial Least Squares (PLS) path modelling. Based on the research findings, servant leadership had a higher influence value of 30.5% in influencing service quality than work commitment, which had a value of 22.4%. Mediation testing using the Sobel test resulted in a t-statistic value of 2.33 1.96 and a one-sided p-value of 0.00 0.05. Work dedication is an effective mediating component to increase the impact of servant leadership on service quality.
Usability Evaluation of Virtual Reality Metaverse Lab Using Usability Testing With the User-Centered Design Approach Kartini, Ketut Sepdyana; Yusa, I Made Marthana; Adnyana, I Nyoman Widhi; Putra, I Nyoman Tri Anindia
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.v5i4.1497

Abstract

Advances in educational technology have introduced immersive tools such as Virtual Reality (VR) to support complex learning, especially in abstract fields like chemistry. Metaverselab was developed to provide a safe virtual environment for conducting laboratory experiments, yet early implementation revealed substantial usability issues. Students experienced difficulties with the interface, confusion in navigation, and insufficient guidance, which affected the overall learning experience. This study aims to evaluate and improve the usability and user experience of the Metaverselab platform using a User-Centered Design (UCD) approach. The research applied an iterative process involving 40 student respondents and used two standardized instruments: the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ). Both instruments were administered during the initial and final evaluation stages to measure changes in usability and user experience after improvements were introduced. Initial results showed low usability, with an average SUS score of 50.81 and UEQ benchmark values categorized as “Bad” in all dimensions. Based on these findings, specific user requirements were identified and translated into targeted design solutions. Key improvements included an interactive tutorial for first-time users, descriptive information pop-ups for laboratory tools, and undo–redo functions to support error recovery during experiments. Post-implementation testing demonstrated substantial improvement. The average SUS score increased by 44.16% to 73.25, placing the system in the “Good” usability category. UEQ results also improved significantly, with the Efficiency dimension rated “Excellent,” while Attractiveness, Perspicuity, and Stimulation were rated “Good.” These results confirm that the UCD approach effectively identifies user needs and produces design interventions that enhance the usability, efficiency, and overall learning experience of virtual educational platforms.
Big Data and Data Mining for Efficient Energy Storage and Management Nazar, Mustafa; Ali, Zaid Ghanim; Adnan, Kahtan Mohammed; Khalil, Ibraheem Mohammed; Nassar, Waleed; Maidin, Siti Sarah
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.v5i4.1759

Abstract

The rapid expansion of decentralized and renewable energy systems necessitates intelligent strategies for energy storage and management. This paper presents a comprehensive framework that leverages big data analytics and data mining to optimize energy storage systems within smart grid architectures. By integrating high-frequency data from IoT-enabled Li-Ion batteries, flow batteries, supercapacitor arrays, and hybrid systems, our methodology enhances storage efficiency, predictive accuracy, and fault detection. The approach uniquely combines an ensemble forecasting model (Random Forest and XGBoost), which achieved a 97% R² score in predicting energy demand, with Gaussian Mixture Models for consumer pattern clustering and canonical correlation analysis to model the impact of environmental variables. Validation on real-world datasets demonstrates significant performance gains without additional hardware. For instance, algorithmic optimization improved the round-trip efficiency of a Hybrid Battery Energy Storage System from 86.7% to 93.3% and a Li-Ion battery by 7%. The study underscores the critical influence of contextual variables like temperature and humidity on state-of-charge stability. Furthermore, the analytical framework demonstrated a 50% increase in system throughput (from 34 to 51 tasks/sec) after optimization. This research provides a replicable, data-driven model for deploying intelligent analytics in both microgrid and industrial-scale settings, paving the way for more adaptive and resilient energy infrastructures. Future work will explore edge computing and reinforcement learning to further enhance scalability and autonomy.

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