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Dahlan Abdullah
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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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Evaluating User Experience of a Virtual Reality-Based Adaptive Learning Application on Chemical Compound Structures for High School Students Setiawan, Esther Irawati; Machfudin, Mohammad Farid; Saputra, Daniel Gamaliel; Santoso, Joan; Gunawan, Gunawan; Kusuma, Samuel Budi Wardhana
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.v5i4.1445

Abstract

Recognizing the significant spatial visualization challenges that high school students face in understanding abstract chemical compound structures—a limitation often inherent in conventional teaching methods based on 2D diagrams—this research presents the comprehensive development and user experience (UX) evaluation of an innovative adaptive learning application in Virtual Reality (VR). The application, developed using the Unity 3D engine and configured via XR Plugin Management to ensure broad hardware compatibility, places students in an interactive virtual laboratory. Within it, students can directly manipulate meticulously designed 3D atomic models to build molecules, observe the formation of covalent and ionic bonds, and interact with dynamic chemical processes. Its key innovation is the integration of an intelligent adaptive learning algorithm, which utilizes a Firebase cloud database to analyze user performance metrics—such as accuracy, completion time, and recurring areas of difficulty. Based on this data, the system dynamically personalizes learning pathways by recommending remedial content or more challenging topics. Furthermore, assessment materials such as quizzes were efficiently generated using large language models (LLMs) to ensure relevance and quality. An in-depth UX evaluation was conducted with high school students using a mixed-methods approach, combining standardized questionnaires to quantitatively measure metrics like usability, engagement, and satisfaction, with qualitative feedback sessions for contextual insights. The results indicate a highly positive user experience; participants reported that the ability to directly manipulate molecules in 3D space significantly enhanced their conceptual understanding, bridging the gap between theory and visualization. The adaptive system was highly valued for its ability to adjust to individual learning paces, which was shown to boost confidence and reduce frustration. This research provides strong evidence that VR-based adaptive learning platforms are powerful pedagogical tools, capable of transforming chemistry education by making complex scientific concepts more accessible, engaging, and comprehensible.
Immersive Digital Data Asset as a Digital Preservation Model for Tangible and Intangible Culture Towards the Indonesia Archipelago Metaverse Iswara, Ida Bagus Ary Indra; Sarasvananda, Ida Bagus Gede; Pramartha, Cokorda
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.v5i4.980

Abstract

This research embarks on a critical mission to develop and comprehensively evaluate the Immersive Digital Data Asset (IDDA) model, positing it as an innovative and robust cloud-based framework. This framework is meticulously designed for the digital preservation of Indonesia's exceptionally rich and diverse tangible and intangible cultural heritage, a crucial endeavor situated within the rapidly emerging and transformative landscape of the Indonesia Archipelago Metaverse. The study directly confronts and seeks to mitigate significant, persistent challenges prevalent in current cultural digitalization efforts across the nation. These challenges notably include the pervasive limited access to advanced technological infrastructure in many remote and underserved areas, compounded by the inherently high development and ongoing maintenance costs associated with cutting-edge immersive technologies. To achieve its objectives, this research rigorously employs a Systematic Literature Review (SLR) methodology. This approach allows for a meticulous analysis of existing academic trends, a systematic identification of crucial gaps within the current literature pertaining to digital cultural preservation, and a synthesis of best practices. The compelling findings unequivocally demonstrate the profound and multifaceted potential of the IDDA model. It serves not only as a powerful tool for safeguarding invaluable cultural assets from degradation and loss but also as a catalyst for fostering dynamic interdisciplinary research collaborations, both domestically and internationally. Furthermore, the model is shown to significantly stimulate the creative economy across Indonesia by enabling new forms of cultural expression and monetization within the digital realm. Nevertheless, the successful, widespread, and sustainable long-term implementation of the IDDA model is critically contingent upon substantial and concerted improvements in technological accessibility nationwide, ensuring equitable participation. This also necessitates a sustained, strategic, and collaborative investment from various stakeholders in its continuous development, infrastructure enhancement, and content creation to fully realize its transformative potential for Indonesia's cultural future.
CrossTrans-Surv: An Artificial Intelligence-Based Multimodal Cross-Attention Transformer for Smart Surveillance and Human Activity Recognition Prasad Reddy, S R V
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.v5i4.1241

Abstract

Classifying and comprehending human behavior in the information provided is known as human movement detection. There are numerous real-world uses for it. Human movement tracking can be used in residential surveillance to monitor senior citizens' behavioral patterns and quickly identify risky behaviors such as falls. It can also assist an automated navigation system in analyzing and forecasting walking patterns. Notably, this system exhibits resilience against changing conditions like weather or light, whereas camera-based approaches falter in these situations. This study presents the AI-based cross-attention transformer framework for multimodal sensor fusion in smart surveillance and human activity detection systems, referred to as CrossTrans-Surv. CrossTrans-Surv, which draws inspiration from STAR-Transformer, integrates asynchronous visual (RGB), infrared/thermal, and LiDAR modalities via cross-attention layers that discover common representations across various data types. Pairs of multispectral images can offer combined knowledge about increasing the robustness and dependability of recognition applications in the real world. In contrast to earlier CNN-based studies, our network uses the Transformer approach to integrate global contextual information as well as learn dependencies that span distance during the feature extraction step. Next, we feed Transformer RGB frames and component heatmaps at various time and location qualities. We employ fewer layers for attention in the framework stream since the skeleton heat diagrams are important features as opposed to those initial RGB frames. Our methodology is appropriate for real-world AI-powered surveillance applications because it provides comprehensibility through consideration maps and scalability through modular design, in addition to performance advantages.
Screen Reader AI: A Conversational Web-Accessibility Assistant for Blind and Low-Vision Users Patel, Rushilkumar
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.v5i3.1562

Abstract

Blind and low-vision users continue to face significant challenges when interacting with modern dynamic and visually complex web applications. Traditional screen readers often fall short due to the rapid changes in content, single-page applications, and intricate layouts. This paper introduces Screen Reader AI, a novel conversational web accessibility assistant implemented as a browser extension, designed to provide adaptive and context-rich support for non-visual navigation. Unlike conventional screen readers, Screen Reader AI constructs and continuously updates a live semantic scene graph by integrating the Document Object Model (DOM) and the Accessibility Object Model (AOM). Leveraging multimodal vision-language reasoning powered by GPT-4o, it generates detailed visual interpretations, detects interface structures and interactive elements, and conveys this information through natural, conversational dialogue. This approach allows users to request clarifications, discover relationships between interface components, and receive proactive notifications about dynamic content updates. The system features a modular architecture that ensures compatibility with evolving AI models and web standards, while maintaining an intuitive user interface. Core capabilities include adaptive task guidance, an interactive dashboard with contextual summaries, nested menus, live feeds, and predictive navigation assistance across diverse content types such as forms and multimedia. An evaluation framework outlines expected improvements in user experience, including reduced task completion times, enhanced understanding of page layouts, and greater autonomy during browsing. Initial findings suggest that conversational interaction can decrease cognitive load by reducing repetitive commands and streamlining information retrieval. Screen Reader AI represents a paradigm shift in digital accessibility by embedding adaptive intelligence into assistive technology, empowering independence and inclusivity while making accessibility an integral part of web innovation.
Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar, Yunidar; Melinda, Melinda; Putri, Mauliza; Irhamsyah, Muhammad; Basir, Nurlida; Khairah, Alfita
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.v5i4.1253

Abstract

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.
Hybrid Graph Attention Networks for Influencer Ranking in Student Activity Networks Setiawan, Mikhael; Santoso, Ong Hansel; Chandra, Iwan
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.v5i4.1474

Abstract

Detecting influencers in a social network of massive student activities is vital for universities because it will help them understand potential leaders and social behavior. This paper mitigates the issues of classical topology-based metrics by presenting volume calculation through Graph Attention Networks (GATs) applied to a real network with 2,520 students and about 282,000 interactions. A new hybrid method of influencer ranking proposed, which combines the node embeddings obtained by GAT with a structural influence signal from PageRank. The evaluation system includes two main parts. First, qualitative evaluation of the hybrid ranking method against PageRank-only. This assessment learns from a ground truth dataset of 993 formal leaders. Second, evaluate the communities found by GNNs against those discovered by classical methods using internal quality criteria, including modularity and conductance. From the observation, PageRank baseline does slightly better than the hybrid method in ranking and both methods are significantly better from a random rank with their Spearman’s Rank Correlation equal to 0.513 for PageRank based and 0.451 of the hybrid variant, respectively. Yet, in the task of community detection, GNNs have greater representational capacity. Even though the resulting modularity score was also very competitive, communities had much lower (and hence better) average conductance than Louvain and Walktrap methods (0.137 vs 0.198 and 0.302). These paired results shows that: the success of a PageRank baseline is tied to our formal-role-based ground truth which is structural. The GNN’s increased ability to discriminate such well-delineated, socially close communities implies that the embeddings it learns better represent the network’s true social structure. In conclusion, while PageRank effectively reveals the formal leaders in a community, our hybrid GAT technique acts as complement to shed light on emerging influencers.
Development of Application-Based Interactive Learning Media in Automotive Engineering Anwar, Choyrul; Umara, Andi Maga; Nurtanto, Muhammad; Sutrisno, Valiant Lukad Perdana; Nendra, Fadly; Febriyanto, Rusdi
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.v5i4.1538

Abstract

Learning media significantly impacts the effectiveness of the learning process. The automotive industry is experiencing rapid development, resulting in a high demand for technicians with expertise in the automotive field. The purpose of this study was to test the development of Smart Apps Creator (SAC) to create interactive learning resources for Engine Management Systems (EMS). This research and development (RD) project used the ADDIE development approach. Five subject matter experts and five media specialists comprised an expert assessment group that validated and evaluated the media to ensure its feasibility. In addition, 62 students reviewed the learning materials as users of the application. The media specialists' evaluation of the feasibility of the learning media construction resulted in a score of 4.02, which is considered practical. The subject matter experts' evaluation of the feasibility of the learning media material resulted in a score of 4.15, which is considered practical. The students' evaluation of the acceptance of the learning media as users resulted in a score of 4.07, which is classified as practical. Meanwhile, the application implementation in the learning process proved to increase learning success among students who used the program development. All things considered, the findings of this study can be used as evidence that application-based learning materials are worthy of widespread use, which will further improve teaching standards. Furthermore, vocational teachers must innovate in developing learning media by utilizing and integrating technology into the process. The creation of such media is necessary in the 21st century to provide easily accessible and understandable learning materials so that students can effectively absorb the information.
A Systematic Literature Review of Technopreneur Ship Fashion Design Purnama, Rahayu; Prabawati, Melly; Radiona, Vivi; Sesnawati, Yeni; Tajuddin, Rosita Mohd; Noor, Muhamad Aiman Afiq Mohd
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.v5i4.1526

Abstract

The fashion industry requires Technopreneur fashion design to face the challenges of the technology and digitalization era in preparation for the 5.0 industrial age. Entrepreneurship dimensions, namely autonomy, innovativeness, risk-taking, proactive, and technology proficiency, are confident insufficient for Technopreneur ship due to their limited resources and lack of knowledge and access to foreign Technopreneur ship in fashion design. This study identifies and reviews the literature on Technopreneur ship orientation fashion education in Scopus between 2010 and 2025. This study identifies and reviews literature on technopreneurs’ orientation from scientific domains: entrepreneurial dimensions and entrepreneur-based technology drivers in fashion design. A systematic approach was adopted, recognizing 25 relevant articles from published journals indexed by Scopus collected from 2010 to 2025. The lack of literature on the technopreneur dimension has resulted in 10 dominant and representative articles originating from Scopus indexed journals and other related journals indexed by Google Scholar collected from 2002-2025. This study consists of two essential parts: 1) descriptive analysis, discussing the characteristics of the related articles, the country of the study, and the methods used. 2) thematic analysis, discussing the six essential categories of drivers in the adoption of technopreneurship and more deeply. The results regarding creating future ideas mean the same as autonomy, business innovation is innovation in the entrepreneurial dimension, seeking opportunities means being proactive, creating new businesses means the same as risk-taking, and technological proficiency are common expressions from several literatures regarding views on technopreneurship itself that do not originate from the entrepreneurial orientation that exists in the previous literature. This study proposes a conceptual framework of technopreneur orientation in fashion design education to develop a sustainable fashion design curriculum in the future.
Cross Modal-FT Net: A Multimodal Fake News Detection Framework using Text, Images, and User Behavior Karnan, K; Aravind Babu, L.R
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.v5i4.1245

Abstract

An unprecedented proliferation of fake news across digital platforms is a major hurdle for reliable information, people trust, and social stability. Current fake news detection techniques, primarily based on text analysis, frequently overlook the multimodal and behavioral indicators associated with contemporary misinformation. Multimodal approaches are rarer and typically classify news as either genuine or fraudulent. To address this problem, this paper proposes a CrossModal-FTNet (Fake News Transformer Network), a transformer-centric multimodal system that identifies fake news by analyzing text, associated images, and user actions such as likes, shares, and the reliability of sources. The suggested model includes three dedicated encoders: a BERT-inspired text encoder for contextual interpretation, a ResNet-50-inspired image encoder for visual cues, and a lightweight behavioral feature encoder for examining user interaction information. These varied representations are subsequently merged through a cross-modal fusion transformer, which synchronizes and enhances data from various sources into a single united feature space. Experiments on benchmark datasets such as Fakeddit, Weibo, MM-COVID, and Twitter15 indicate that the suggested model excels, attaining 94.3% accuracy and a 92.8% F1-score, outpacing multiple unimodal and early fusion baselines. The findings confirm that using cross-modal data greatly boosts the ability to detect fake news. Thus, CrossModal-FTNet offers a scalable, real-time, and precise solution for combating misinformation in the ever-changing online environment.
Hybrid CNN-LSTM Model for Predictive Maintenance of Wind Turbine Systems Jiang, Qi
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.v5i4.1679

Abstract

Predictive maintenance enhances the reliability and efficiency of wind turbine systems through its role in managing these wind energy systems, which represent the most commonly used renewable resource worldwide. This research develops a combined Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework to refine fault detection as well as maintenance tactics using Supervisory Control and Data Acquisition (SCADA) measurements. Through its spatial pattern extraction ability, CNN operates on multivariate sensor data, while LSTM maintains temporal dependencies to recognise complex time-dependent degradation patterns. The proposed Hybrid CNN-LSTM model achieved outstanding predictive maintenance performance for wind turbines with an accuracy of 96.5%, precision of 96%, and recall of 95.5%. It outperformed CNN (accuracy: 91%), LSTM (89.5%), and Random Forest (83.5%) in all key metrics. The model also achieved the highest F1-score (96%) and AUC (0.96), proving its reliability in real-time fault detection. Verification of the methodology involves testing it on real SCADA data from two wind farm sites over two years, where it proves capable of spotting abnormal operations at early stages. Secure wind energy operations, along with efficient cost reduction, become feasible through the use of this solution, which reduces unexpected equipment failures while minimising downtime events.

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