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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
Entrepreneurial Intention of MSME Actors in Indonesia: An Empirical Study on the Influence of Entrepreneurship Learning and the Moderating Role of Subjective Norms Gunawan, Indra; Indradewa, Rhian; Kustiawan, Unggul
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.v5i4.1484

Abstract

Entrepreneurship is one of the strategic pillars in driving national economic growth, particularly through the role of Micro, Small, and Medium Enterprises (MSMEs), which contribute more than 60% of Indonesia’s Gross Domestic Product (GDP). This study aims to analyse the factors affecting entrepreneurial intention among MSME actors in Indonesia who have participated in entrepreneurship training. Specifically, the study examines the influence of entrepreneurial motivation, market orientation, entrepreneurial orientation, entrepreneurial learning, entrepreneurial attitude, and entrepreneurial self-efficacy on entrepreneurial intention, as well as testing the role of subjective norms as a moderating variable. Using a quantitative approach and the Partial Least Squares Structural Equation Modelling (PLS-SEM) method, data were collected from 380 MSME respondents across the Greater Jakarta area. The findings reveal that entrepreneurial learning significantly mediates the relationship between market orientation and entrepreneurial intention, as well as between entrepreneurial motivation and entrepreneurial intention. Meanwhile, subjective norms were found to moderate the relationship between entrepreneurial attitude and entrepreneurial intention, but not the relationship between entrepreneurial self-efficacy and intention. These findings contribute theoretically to the understanding of the cross-path relationships between psychological and contextual variables in shaping entrepreneurial intention. In practical terms, entrepreneurship training should be designed to strengthen active learning and foster social norms that support entrepreneurial intention.
Evaluation of the Quality and Safety of Smoked Fish Produced Using a Modified Efhilink Smoking Cabinet With Different Bio-Smoke Sources Joesidawati, Marita Ika; Suwarsih, Suwarsih; Sriwulan, Sriwulan
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.v5i4.1476

Abstract

Traditional fish smoking methods often raise significant concerns regarding product safety, quality inconsistency, and environmental pollution. This study aimed to evaluate a modified Efhilink smoking cabinet designed to address these issues by utilizing agricultural waste, specifically corn cobs and coconut shells, as bio-smoke sources for producing high-quality, safe smoked fish compliant with the Indonesian National Standard (SNI 2725:2013). Three fish species (mackerel tuna, Euthynnus affinis; flying fish, Cypselurus spp.; and ray, Dasyatis spp.) were processed using the modified cabinet and a traditional cabinet (control) and subsequently analyzed for sensory properties, proximate composition, histamine, TVB-N, pH, total phenolic content, and various contaminants (microbiological, heavy metals, chemical residues, and polycyclic aromatic hydrocarbons (PAH4)). The results demonstrated that all smoked fish samples from the modified cabinet met all critical parameters of the national standard. Coconut shell smoke generally yielded superior products, characterized by higher acceptability in aroma and taste, a greater infusion of phenolic compounds (up to 0.334 mg/kg), and significantly lower levels of PAH4 contaminants compared to the traditional control. All samples from the modified cabinet exhibited histamine levels well below the 100 mg/kg safety limit (12.36–19.37 mg/kg), total plate counts within the permissible range (10 to 3.6x10? CFU/g), and a complete absence of detectable pathogens (E. coli, Salmonella spp., S. aureus, V. cholerae) or hazardous chemical residues (chloramphenicol, malachite green, nitrofuran); heavy metal contaminants were also found at levels far below the maximum allowable limits. The modified cabinet significantly outperformed the traditional method in reducing PAH4 contamination. The technology not only enhances food safety but also promotes sustainable practices by converting agricultural waste into value-added products. In conclusion, the modified Efhilink cabinet, using either corn cob or coconut shell bio-smoke, effectively produces safe, high-quality smoked fish that complies with stringent food safety standards, with coconut shells demonstrating superior performance as a smoke source by enhancing sensory attributes and bioactive compound content while minimizing hazardous contaminants.
Investigating the Energy Cost for n Wireless Sensor Network using IoT by Implementing RMP Algorithm K, Kishore Kumar; Srinivasulu, G.
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.v5i4.1243

Abstract

Internet of Things (IoT) sensor networks frequently see energy savings since the nodes in the network are powered by their own finite batteries. While data processing uses a lot less energy than data transmission in IoT sensor nodes is expensive energy-intensive. Over the last few years, wireless sensor systems based on IoT has witnessed an evolutionary breakthrough across several industries  various sectors. The Internet of Things, or IoT, is a network that allows physical items, machinery, sensors, and other devices to communicate with one another without the need for human intervention. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. Nowadays, the critical and non-critical applications of the IoT and WSNs affect nearly every part of our daily life. WSN nodes are usually small, battery-powered machines. Therefore, Energy-efficient data aggregation techniques that prolong the network's lifespan are crucial. Reducing data transmission is the primary goal of many energy-saving techniques and concepts. As a result, significant energy savings can be achieved in IoT sensor networks by reducing data transfers. The proliferation of IoT-based Wireless Sensor Network has triggered a paradigm shift in the business, necessitating the use of dependable and efficient routing techniques. A compression-based data reduction (CBDR) method that operates at the level of IoT sensor nodes was proposed in this study. To recover the data at the sink or BS end, we suggest using a Randomised Matching Pursuit algorithm. Additionally, beneficial is the use of CLH and relay routing.
Designing and Validating an Instrument to Assess Home Literacy Environment in Early Childhood: A Confirmatory Factor Analysis Oktaviani, Maya; Elmanora, Elmanora; Silitonga, Mirdat; Mashabi, Nurlaila A; Muchtar, Eka Nur Pebriyanti; Marjan, Lu'lu' Wal
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.v5i4.1540

Abstract

Strengthening literacy from preschool age impacts children's social, emotional, and critical thinking development. This activity aligns with the Sustainable Development Goals, particularly SDG 4, which targets quality and inclusive education for all children. In early literacy development in preschool-aged children, the environment closest to them plays a significant role: the family and school. Therefore, this study aims to develop a Home Literacy Environment (HLE) instrument for preschool-aged children using Confirmatory Factor Analysis (CFA). The study employs a research and development methodology, specifically the 4D model (Define, Design, Develop, Disseminate), to produce a standardized measurement tool. Validation procedures were conducted in three stages: construct validation by three experts, content validation by 14 panellists, and empirical testing involving 165 families with children aged 5–6 years in the Greater Jakarta area. Data were analyzed using CFA to examine factor structure and construct validity. Results indicated that 20 items across the three core dimensions demonstrated adequate factor loadings and significant t-values, with high construct reliability and variance extracted, confirming their validity. Nine indicators of goodness of fit met the criteria. Overall, the model was deemed sufficiently fit and suitable for further interpretation. This study supports the broad applicability of the HLE as a valid measure of the literacy environment created at home for preschool children. By providing a validated HLE instrument, educators, researchers, and policymakers are equipped to assess and enhance the literacy support provided at home. This result enables targeted interventions and informed decision-making to strengthen early learning foundations and promote inclusive, equitable education from the earliest years.
Analysis of the Influence of Knowledge Management, Digital Adoption, and Technology-Based Training on Organizational Performance Widiantoro, Didik; Judijanto, Loso; Suhartono, Suhartono; Pramono, Susatyo Adhi; Dewa, Dominica Maria Ratna Tungga; Februati, Bernadin Maria Noenoek
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.v5i4.1222

Abstract

The manufacturing industry in Indonesia faces significant challenges in the era of digitalization and technological transformation. To remain competitive, companies need to implement strategies that support improved organizational performance. This study aims to analyze the influence of knowledge management, digital adoption, and technology-based training on organizational performance. The study was conducted using a quantitative approach with a survey method of 130 employees from six manufacturing companies in Indonesia. Data were collected through closed-ended questionnaires and analyzed using multiple linear regression. The results of the analysis indicate that all independent variables significantly influence organizational performance. Knowledge management is the most dominant factor with a beta coefficient value of 0.438, followed by digital adoption (0.386) and technology-based training (0.342). The Adjusted R² value of 0.675 indicates that the three variables explain 67.5% of the variation in organizational performance. Furthermore, all proposed hypotheses are accepted based on significance values below 0.05. This study confirms the findings of previous studies, which demonstrate the importance of knowledge and technology management in driving organizational competitiveness. These findings provide practical recommendations for manufacturing company management to integrate knowledge management systems, accelerate technology adoption, and increase the effectiveness of technology-based training to improve organizational performance sustainably.
Smart Stego: A Web Application for Hiding Secret Data in Images with LSB and CNN Suryawan, I Gede Totok; Sudarma, Made; Putra, I Ketut Gede Darma; Sudana, Anak Agung Kompiang Oka
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.v5i4.1008

Abstract

This study develops a web-based steganography model to insert the identity of artisans in the form of palmprint images into the image of gringsing ikat woven cloth as a medium for ownership authentication. The method used in the insertion process combines a Convolutional Neural Network and the Least Significant Bit. In contrast, extracting or re-introducing palmprint images from stego images is carried out using a CNN-based classification model. This system was tested with two scenarios; in the first scenario, one palmprint image was inserted into 26 different cloth motifs, while in the second scenario, one cloth motif was inserted into 99 different palmprint images. The test results showed that the system produced consistent confidence values for all cloth motifs in the first scenario. In contrast, in the second scenario, the system achieved an average confidence of 93.5% and a recognition accuracy of 87%. The developed application has proven to be efficient with a reduction in stego image size of up to 66% while maintaining the quality of the stego image, as well as a speedy average execution time of 0.15 seconds for insertion and 0.09 seconds for extraction. These findings prove that the developed steganography model can effectively insert and re-recognize identity images (palmprints) in woven cloth images and has the potential to be applied as an image-based craft product ownership verification system.
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.
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.

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