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 593 Documents
 45   46   47   48   49 
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda, Melinda; Yunidar, Yunidar; Miftahujjannah, Rizka; Rusdiana, Siti; Amalia, Amalia; Qadri Zakaria, Lailatul
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.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.
Neuromorphic Hardware Design for Energy-Aware Artificial Intelligence Computation Aljanabi, Yaser Issam Hamodi; Hussain, Salah Yehia; Salim, Darin Shafiq; Al-Doori, Vian S.; Brieg, Jassim Mohamed; Batumalay, M.
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.v5i1.1279

Abstract

Rapid growth of the energy-efficient artificial intelligence (AI) systems has attracted substantial interest in neuromorphic computing that emulates organization and actions of a biological neural?system to support low-power, event-driven information processing. In this work, we propose a neuromorphic hardware architecture for energy-efficient AI computing that utilizes spiking neural networks and monolithic?vertical integration to improve the performance of a variety of vision tasks. The architecture is tested against three benchmark datasets— MNIST, N-MNIST, and DVS128,?representing static, spiking and dynamic input modalities, respectively. The performance metrics, such as energy efficiency, inference latency,?throughput, classification accuracy, and unified Energy Efficiency Index (EEI) are compared to characterize the generalization power of the system in different processing environments. Experimental results show that the proposed chip provides a sharply lower energy per inference with a competitively performing accuracy over conventional AI?accelerators, including GPU-based and microcontroller platforms. Additionally, the hardware achieves sub-2 ms inference latency and high throughput, indicating suitability for real-time, embedded AI applications. Comparative analysis with existing neuromorphic platforms highlights the advantage of architectural co-design in balancing energy and performance constraints. While the absence of on-chip learning presents a limitation, the system offers a scalable foundation for edge AI systems requiring efficient, continuous inference. Future directions include integrating adaptive learning mechanisms and extending evaluation to broader AI domains as a process innovation.
Smartphone Dependence and Academic Stress: A Psychological Analysis Among College Students Karimova, Shoira; Nuritdinova, Khurshida; Sabitova, Nailya; Babayeva, Irada; Sabirov, Sardor; Hakimova, Nasiba; Isroilova, Bakhtijon
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.v5i4.1215

Abstract

The General Stress Theory (GST) posits that stress results in many inappropriate actions. This research examined the correlation between Perceived Stress (PS) and dependence on Smartphones (SP). The study posited that this correlation is mediated by Diminished Self-Control (SC) and the first pathway of the mediating factor, which is influenced by safety. A survey was conducted using cluster sampling techniques on 400 undergraduates at an educational institution in Uzbekistan. The pupils were administered the Smartphone Addicted Scale-Short Variant (SAS-SV), the Depressive Anxiety Stress Score (DASS), the SC Scale (SCS), and the Safety Questionnaire (SQ) throughout scheduled class periods. The statistical program facilitated qualitative statistics and Pearson correlation evaluations. At the same time, the research was employed to examine the mediating impact of SC and the regulating influence of safety. The mediation study indicated that, as anticipated, PS correlated with less SC, which correlated with an increased risk of dependence on SP. As expected, moderated mediation analysis revealed that the relationship between PS and SC was influenced by security. The correlation between felt anxiety and SC was more pronounced in conditions of poor security. This research offers valuable insights into the relationship between PS and the heightened risk of dependence on SP. The findings align with the GST and suggest that tangible strategies are necessary for the avoidance and treatment of addiction to SP among undergraduates.
Enhancing Multi-Label News Text Classification for an Understudied Language: A Comprehensive Study on CNN Performance and Pre-Trained Word Embeddings Rundasa, Diriba Gichile; Ramu, Arulmurugan
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.v5i4.987

Abstract

Today's news texts are classified using a multi-label system, which allows for the assignment of a potentially large number of labels to specific instances. The majority of earlier scholars have only looked into mutual exclusion at a single level. Nonetheless, the primary goal of this study was to categorise the news material using multiple labels. Many text documents are created these days from a variety of offline and internet sources. This generated news text is disordered state. As a result, timely access to the needed content from the sources is challenging. Compared with traditional text classification, multi-label classification is difficult and challenging because of its multi-dimensional labels. Convolutional neural networks are used in this study's tests on the problem domain for Afaan Oromo multi-label news text classification due to their ease of assimilation of pre-trained word embeddings. According to pre-trained word embedding with a train-test ratio of 10/90, the new proposed model has shown improved performance. The suggested CNN models might be helpful for labelling news articles in Afaan Oromo news text. The goal of many researchers working on Afaan Oromo classifier development is to use various learning algorithms to boost classification accuracy as the number of categories or labels increases. Using various approaches, they attempted to use basic machine learning methods to address the calculation time issue. Unfortunately, all low-resource language researchers focus on flat, hierarchical, and multi-class classification types, but we created a model for multi-label text classification and attempted to apply it using a deep learning algorithm. Over 5640 Afaan Oromo news dataset items are analysed experimentally over eight main news categories. Python served as our experimental platform for both text classification and word embedding. After the model is fully implemented, the best result of the precision, recall, F1 score and accuracy rate train test ratio of 10/90 for pertained word_ embedding is 89.7%, 88.6%,  93.3% and 96.5, respectively.
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.
Soft Robotics with Quantum-Driven Electronic Neural Networks Rahman, F.; Mishra, Nidhi; Bansal, Bhumika
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.v5i2.1391

Abstract

Although this field attracts lots of attention, conventional control mechanisms of Soft Robotics are still restricted in real-time decision making, learning efficiency, and energy consumption. This research further strengthens soft robotic intelligence to present a novel Quantum Driven Electronic Neural Network (QD-ENN) framework based on Design of Reservoir Computing (QRC) to be used for development of Brain of the Offspring (BO) and contextual entangled processing (CEPT) nodes. Quantum superposition and entanglement make it intrinsically superior to sensorimotor learning, low power computation, and rapid adaptation of the sensorimotor interaction in an unstructured environment. Compared with classical deep learning methods that require huge quantities of training and computations to learn, the proposed system solves real-time control problem and changes morphologies of soft actuators dynamically using quantum inspired neural plasticity. Based on the design of the architecture, which is implemented for neuromorphic processing using memristor electronic synapses and based on quantum circuits to help with reinforcement learning, the architecture designed employs quantum circuits and memristor electronic synapses. Experimental evaluations also demonstrate excellent speed up in terms of learning speed, decision accuracy and energy efficiency compared to the traditional AI-driven soft robotic controllers. Based on this work, future research on quantum neuromorphic architectures in robotics follows by building semiconductor hardware towards self-learning robotics of exceptionally dynamical and unpredictable nature.
Social Media: A Bibliometric Analysis Maharani, Siska; Lutpiani, Anisa; Setiawan, Adi
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.v5i2.880

Abstract

This research discusses the development and role of social media in modern life, especially Within digital marketing and consumer behavior. Social media, defined as internet-based platforms that enable user creation and exchange of content, has experienced a rise from 2003 to 2013, with significant influence on communication, social interaction, and business strategy. The method used in this study is bibliometric analysis with VOSviewer software, which maps data from scholarly articles published in leading journals such as Elsevier, Emerald, Springer, Willey, Sagepub, and Taylor. Data was collected using Publish or Perish software with specific keywords related to social media, digital marketing, and consumer behavior. From the bibliometric analysis, 1,550 articles with 4,961 keywords were found and analyzed using co-occurrence and co-authorship methods, resulting in 111 specific keywords divided into 11 clusters. The study revealed the importance of social media in building customer-business relationships, the influence of the power of social ties on consumer behavior, and the role of social media in developing effective marketing strategies. The research also identifies author collaborations in social media-related studies and shows how research trends and patterns evolve. The findings provide deep insights into the use of social media in modern marketing strategies. They can form the basis for further research development in management, business, and digital marketing. The findings of this study offer valuable contributions to the academic and professional understanding of social media. They serve as a foundational reference for future research aiming to explore the strategic applications of social media in business, the psychology of online consumer engagement, and the technological advancements that continue to reshape digital communications. The study also provides insights into the evolution of research patterns, visualizing how scholarly focus has shifted from early platforms like MySpace and Flickr to contemporary and emerging platforms such as YouTube, TikTok, and Instagram. Using overlay visualization and keyword density maps, the research identifies recent trends and research gaps, highlighting areas that require further academic exploration. For instance, the dominance of "YouTube" in recent literature indicates a growing interest in multimedia marketing, influencer culture, and community-driven content. At the same time, some topics, such as mobile-integrated social platforms, remain underexplored.
Quantum Machine Learning for Enhancing Signal Processing Applications Sairam, Kamineni; Deepak, Shashikant; Chakravarthi, Rekha; Mohanty, Saumendra Ku.; Rao, P.S. Raghavendra; Choudhary, Varsha; Punia, Ankit
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1375

Abstract

In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Predictive Data Analytics for Fault Diagnosis and Energy Optimization in Industrial IoT Environments Fallah, Dina; Abdul-Kareem, Bushra Jabbar; Murad, Nada Mohammed; Mahdi, Ammar Falih; Janan, Ola; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1392

Abstract

The fusion of predictive maintenance with energy optimization represents a critical advance for intelligent Industrial Internet of Things (IIoT) systems. In response to the growing industrial demand for highly reliable and efficient operations, this study introduces and validates a unified framework that couples fault diagnosis via deep learning with energy management via reinforcement learning. We utilize a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for multivariate fault detection, which demonstrates superior classification accuracy and robustness against data incompleteness. Simultaneously, a Deep Q-Network (DQN) performs dynamic energy scheduling based on predicted system health, achieving substantial energy reductions without compromising task deadlines. Extensive experimental results from real-world industrial datasets and simulations confirm the integrated framework's superiority over conventional approaches in both diagnostic precision and energy efficiency. Key performance indicators, including inference speed and cross-validation, affirm its suitability for real-time industrial applications. This work demonstrates that integrating predictive analytics into intelligent control paradigms is crucial for improving the reliability and sustainability of modern IIoT systems and offers a replicable blueprint for developing next-generation smart manufacturing solutions.
Engineering Waste Management Systems: Efficiency Through Strategic Planning and Management Tools Ugli, Isroilov Sardorbek Solijon; Ghate, Atul Dattatraya
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1385

Abstract

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

Page 47 of 60 | Total Record : 593

 45   46   47   48   49 

Filter by Year

2021 2026


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