cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dahlan Abdullah
Contact Email
dahlan@unimal.ac.id
Phone
+62811672332
Journal Mail Official
ijestyjournal@gmail.com
Editorial Address
Jl. Tgk. Chik Ditiro, Lancang Garam, Lhokseumawe, Aceh - Indonesia, 24351
Location
Kota lhokseumawe,
Aceh
INDONESIA
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 81 Documents
 5   6   7   8   9 
Search results for , issue "Vol 5, No 3 (2025)" : 81 Documents clear
Analysis of the Influence of Infinite Mindset Through Innovation and Learning Ability on Business Sustainability Aulia, Muhammad Reza; Salsabila, Cut; Nasution, Anisah; Fuqara, Fanthasir Awwal; Azrani, Utary; Pratiwi, Henny; Tannady, Hendy
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.v5i3.1181

Abstract

In today's era of globalisation, business development is growing rapidly and undergoing continuous metamorphosis. The coffee business itself has a long history of driving economic growth in Indonesia. Geographically, Indonesia's soil is ideal for the microclimate that supports coffee growth and production. According to the 2023 Indonesian Statistics report from the Central Statistics Agency (BPS), Indonesian coffee production reached 794.8 thousand tons in 2022, an increase of approximately 1.1% compared to the previous year. Methods and approaches in the current era of globalisation have accelerated business development and continued transformation. The coffee industry is a promising sector with significant economic potential, particularly in West Aceh Regency. This study aims to analyse the influence of an infinite mindset on the sustainability of coffee shop businesses, both directly and indirectly through innovation and learning ability as mediating variables. This study used an associative quantitative approach with a sample of 119 coffee shop owners or managers selected using purposive and convenience sampling techniques. Data were collected through questionnaires and analysed using the Partial Least Squares (PLS) method with the help of the SMART PLS 4 software. The results showed that an infinite mindset has a positive and significant effect on innovation and learning ability. Furthermore, both innovation and learning ability also have a positive and significant effect on business sustainability. An infinite mindset was also proven to have a significant indirect effect on business sustainability through these two mediating variables. These findings emphasise the importance of a long-term mindset and continuous learning in facing dynamic business competition.
A Novel Hybrid Method for DAP: Differential Evolution with Variable Neighborhood Search Thakur, Mamta; Sushma, Talluri; Vellanki, Nagaraju; Shareef, R. M. Mastan; Anusha, Peruri Venkata; Swarna, B; Peter, Geno
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.v5i3.1296

Abstract

This research investigates MOPFSP-SDST, an advanced and highly computational scheduling difficulty in real-world manufacturing systems. It examines how it correlates with multi-objective permutation flow shops. LS-MOVNS stands for "Learning and Swarm-based Multi-objective Variable neighbourhood Search." It is a better metaheuristic method that combines evolutionary swarm search and adaptive local search techniques to address this Problem. The two main improvements have been discussed: a partial neighbourhood assessment framework that reduces the computational expenses by analysing only a particular portion of the neighbourhood, and an adaptable neighbourhood series selection procedure that rapidly chooses the most beneficial neighbourhood order depending on past performance rates. These improvements aim to make searches more effective and productive by finding a better balance between exploration and exploitation. Particularly in medium to large problem sizes, experimental tests in benchmark instances show that LS-MOVNS frequently outperforms current modern algorithms in convergence and diversity. The results verify the long-term reliability, scalability, and practical applicability of LS-MOVNS for resolving challenging multi-objective scheduling issues.
Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Ismail, Laith S.; Jamil, Abeer Salim; Ali, Taghreed Alaa Mohammed; Al-Dosari, Ibraheem Hatem Mohammed; Salman, Khdier; Maidin, Siti Sarah
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.v5i3.1397

Abstract

Autonomous electric vehicles (AEVs) require real-time decision-making, low-latency computation, and energy-aware coordination to operate effectively. Traditional centralised cloud computing struggles to meet these demands due to inherent delays and scalability issues in large-scale AEV networks. This paper proposes a novel hybrid edge–fog computing architecture to address these challenges. Our framework utilises a three-tier system (vehicle-edge, roadside-fog, and cloud) governed by a deep reinforcement learning agent that manages energy-aware task offloading. Extensive simulations demonstrate the framework's effectiveness, achieving significant end-to-end latency reductions of up to 56% during urban peak hours and decreasing energy consumption by 20% under high-load conditions. The deep reinforcement learning agent successfully adapts control policies to dynamic road conditions, while the architecture proves highly scalable and resilient, maintaining high task success rates and recovering from node failures in seconds. These findings confirm that a hybrid edge–fog architecture, guided by reinforcement learning, is a highly effective solution for scalable, adaptive, and energy-efficient AEV operations. This study's primary contribution is an empirically validated framework that uniquely integrates predictive control and energy-aware scheduling at the edge, providing a deployable model for next-generation intelligent transportation systems.
Design and Deployment of a Secure Cyber-Physical System for Energy Monitoring in Smart Agriculture Fallah, Dina; Abbas, Elaf Sabah; Ahmed, Mohsen Ali; Sajid, Wafaa Adnan; Al Hilfi, Thamer Kadum Yousif; Maidin, Siti Sarah
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.v5i3.1398

Abstract

The growing need for sustainable agricultural practices has spurred the integration of cyber-physical systems (CPS) into modern farming. This paper presents the design, deployment, and evaluation of a modular CPS architecture for adaptive energy monitoring and control in smart agriculture. The system integrates environmental sensing, predictive modelling, and optimisation-guided actuation to enhance energy efficiency and operational resilience. Field tests on a 3-hectare site across six crop environments demonstrated significant performance gains, achieving energy savings of up to 25.8% and peak demand reductions of up to 19.8%. Our multi-layer architecture, featuring STM32 microcontrollers, LoRaWAN communication, and a cloud analytics dashboard, enables proactive control by anticipating energy demand using an LSTM-NARX predictive model. This approach reduced control actuation delay to 1.8 seconds and proved robust against cyber-physical faults, recovering from communication failures and data anomalies in under 15 seconds. The results validate that embedding energy-aware, predictive logic into CPS infrastructure creates scalable, efficient, and reliable agricultural solutions. We acknowledge limitations related to predictive model complexity and communication latency, and we propose future work focused on distributed CPS coordination, federated learning, and full lifecycle sustainability analysis to further advance intelligent, resource-efficient agriculture.
A Comprehensive Review-Remote Monitoring System Based on Iomt for Neurological Disabilities Raj M S, Pradeep; P, Manimegalai
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.v5i3.1036

Abstract

The vital source of the Internet of Things (IoT) in medical industries is said to be the Internet of Medical Things (IoMT). Currently, IoMT has exponential growth in remote monitoring systems (RMS), mainly for neurological disability patients. The main aim of IoMT is to proliferate the factors of electronic devices such as trustworthiness, efficiency, and accuracy. There exists enormous ongoing research in IoMT in this area, and huge devices are being approached. However, there are different types of neurological disabilities (ND) around the world, and countable IoMT remote monitoring systems were developed for the most common neurological problems. So, this article is fully concentrated on the study of different neurological problems and the RMS-IoMT. This review is essential for many biomedical and medical researchers, and it deals with the doctor’s opinion and the importance of the IoMT system, common neurological disabilities, and the RMS-IoMT system’s merits and demerits for neuro disorders.
A Lightweight Deep Learning Model for Crop Disease Detection on Mobile Devices Jing, Qi
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.v5i3.1535

Abstract

Early detection of crop disease is an important part of modern agriculture since early detection would help in reducing crop loss and improving food security. The purpose of this study is to develop and evaluate lightweight deep-learning models for disease detection using simulation-based data where the output device would be a mobile device. Training and testing three types of machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) on simulated agricultural data of soil health, weather conditions and plant health is a part of the research methodology. To evaluate the models, the accuracy, F1-score and inference time were used. And results indicate that RF and SVM both performed with 100% accuracy (F1 score equal 1.0) whereas the CNN model has 87.5 % accuracy and loss = 0.2279. The CNN model, although it has slightly lower performance, is promising for deployment on mobile as it offers better results. The study concludes that there is room for light-weight CNN models for real-time disease detection on mobile devices. The future study will analyze how CNN architecture can be optimized using real-world data. This study has practical implications for mobile-based solutions for crop disease management in resource-constrained environments. A major weakness is that the data used is simulated data and may not account for the realities of agricultural conditions.
Factory-Grade Diagnostic Automation for GeForce and Data Centre GPUs Lulla, Karan; Chandra, Reena; Ranjan, Kishore
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.v5i3.1089

Abstract

The growing deployment of Graphics Processing Units (GPUs) across data centers, AI workloads, and cryptocurrency mining operations has elevated the importance of scalable, accurate, and real-time diagnostic mechanisms for hardware quality assurance (QA). Traditional factory QA processes are manual, time-consuming, and lack adaptability to subtle performance degradation. This study proposes an automated diagnostic pipeline that leverages publicly available GPU telemetry-like data, including hashrate, power draw, and efficiency metrics, to simulate factory-grade fault detection. Using the Kaggle “GPU Performance and Hashrate” dataset, we implement a machine learning-based framework combining XGBoost for anomaly classification and Long Short-Term Memory (LSTM) neural networks for temporal efficiency forecasting. Anomalies are heuristically labeled by identifying GPUs in the bottom 10% of the efficiency distribution, simulating fault flags. The XGBoost model achieves perfect accuracy on the test set with full interpretability via SHAP values, while the LSTM model captures degradation trends with low training loss and forecast visualizations. The framework is implemented in Google Colab to ensure accessibility and reproducibility. Diagnostic outputs include efficiency analysis, prediction overlays, and automated GPU health reports. Comparative results show higher efficiency variance in GeForce GPUs versus the more stable performance of data center models, highlighting hardware class differences. While limitations exist, such as reliance on simulated labels and static time windows, the study demonstrates the feasibility of ML-driven, scalable diagnostics using real-world data. This approach has direct applications in early fault detection, GPU fleet management, and embedded QA systems in both production and deployment environments.
Operationalizing No-Code AI: Cross-Functional Implementation and Organizational Impact Mukesh Shah, Binita; Bansal, Rishab
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.v5i3.1190

Abstract

This paper explores how non-technical teams can be the form of organizational adoption and quantifiable results of the so-called no-code AI platforms. Through the sequential mixed-method design, 32 organizations in the six industries supplied data complemented by large-volume data sets such as the Stack Overflow Developer Survey (n = 73,268) and Kaggle Data Science Skills dataset (n = 25,973). Hierarchic clustering produced the following three cases of adopters: early adopters in marketing and operations, pragmatic adopters in customer service and HR, and conservative adopters in finance and legal with high adoption differences (37.82-fold asymptotic, p = 0.001). Regression analysis identified functional success predictors like, MarTech integrations of the marketing system-based system (= 0.43, P = 0.001) integration of the operations systems-based system (= 0.52, P = 0.001) and privacy protection-based HR system (= 0.56, P = 0.001). Productivity analysis showed that initial implementation cost decreased output by -7 percentage in the first month, but was compensated in 2-3 and 4-6 months on marketing/operation and other functions respectively. In twelve months, long-term returns amounted to 37 per cent marketing, 31 per cent operations and 26 customer service. Three clusters were verified by calculation of ROI: high ROI in marketing/operations (143%-217%), moderate ROI in customer service (87% -112%), delayed ROI in HR, finance, and legal (31% -64%). A tested implementation model has been constructed, which relies on the use of functional approaches, levels of governance, capability-building and integration methods with good predictive validity (R 2 = 0.71, error rate = 12%). The evidence shows that the democratization of AI can be achieved through strategic alignment, risk-sensitive governance, and role-specific training that would optimize the use of AI and its long-term organizational value.
Toward Ultra-Reliable Low-Latency V2X: A Hybrid Deep Learning Approach for Intelligent Vehicular Networks Jiang, Yi; Bin Ariffin, Shamsul Arrieya
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.v5i3.1536

Abstract

Safe and efficient vehicular networks in contemporary intelligent transportation systems necessitate ultra-reliable and low-latency communication (URLLC) requirements acting as the base foundation. Researchers combined Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks for creating their Hybrid Deep Learning-Based V2X Framework to improve V2X real-time decision-making abilities. The system's first operation phase acquires diverse Vehicle-to-Everything data from V2V, V2I, V2P and V2N sources which contain GPS locations and vehicle speed readings side by side with Received Signal Strength Indicator (RSSI) measurements along with channel status data. The preprocessing method applies normalization strategies (Min-Max Scaling and Sliding Window Method) together with data reduction methods and time-series transformations to create ready-to-use modelling inputs. Through traffic data sources CNN modules decode road layout features and vehicle distributions next to detecting signal interference sequences but LSTM modules analyze signal variations and handover delay effects and identify congested area evolutions. Processor layers integrate both spatial and temporal elements to produce a unified representation that enables predictions for optimal communication standards. The system maintains dependable communication in dense and mobile environments by enabling adaptive routing and dynamic power control along with stable link selection mechanics. The proposed hybrid framework will benefit the next-generation V2X network by achieving computational efficiency alongside predictive accuracy for autonomous driving and smart traffic management functionalities. The proposed hybrid framework boosts the V2X network by ensuring both computational efficiency and predictive accuracy for autonomous driving, enabling improved traffic management. This integration enhances vehicle coordination, real-time safety, and congestion forecasting for future transportation systems.
Mobile Learning Applications and Their Impact on Students' Academic Performance in Rural Schools Muhammedova, Farog‘at; Ergashev, Mirkomil; Imamova, Nilufar; Ashirova, Anorgul; Urishev, Adham; Kuvvatova, Mokhira; Sattorova, Shalola
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.v5i3.1412

Abstract

Mobile learning is a recognised methodology due to its numerous advantages, including the ability to access educational content at any time and place, customisation of content to meet students' needs, and prompt feedback. This study aims to demonstrate that learning facilitated by a customised smartphone application can successfully improve the academic achievement of Rural School (RS) students by implementing periodic evaluations via the mobile application. This study proposed Mobile Learning Applications and their Impact on Students' Academic Performance (MLA-SAP) in RS. The study subjects were students in RS, Uzbekistan. An MLA-based approach was implemented in the test group (n?=?20), whereas the control group engaged in a lecture-based traditional classroom setting (n?=?35). An outlook scale has been employed to assess students' perceptions of mobile learning, while a test of success was utilised to evaluate the impact of MLA on student academic performance. Interviews have been conducted with RS students and teachers for a qualitative analysis. The results indicate that MLA may facilitate SAP. Both groups exhibited markedly elevated scores regarding MLA.

Page 7 of 9 | Total Record : 81

 5   6   7   8   9 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 6, No 1 (2026) Vol 5, No 4 (2025) Vol 5, No 3 (2025) Vol 5, No 2 (2025) Vol 5, No 1 (2025) Vol 4, No 4 (2024) Vol 4, No 3 (2024) Vol 4, No 2 (2024) Vol 4, No 1 (2024) Vol 3, No 4 (2023) Vol 3, No 3 (2023) Vol 3, No 2 (2023) Vol 3, No 1 (2023) Vol 2, No 4 (2022) Vol 2, No 3 (2022) Vol 2, No 2 (2022) Vol 2, No 1 (2022) Vol 1, No 4 (2021) Vol 1, No 3 (2021) Vol 1, No 2 (2021) Vol 1, No 1 (2021) More Issue