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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 593 Documents
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Role of fintech in financial inclusion: A quantitative review Sharma, Mona; Bahl, Jyotika; Gothwal, Pushpender; Kumar, Dev; Atree, Luxmi; Kumar, Sunil
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.v5i4.1133

Abstract

This study presents a comprehensive bibliometric analysis of the scholarly literature on fintech and financial inclusion. Using a structured search strategy, 611 articles published in Scopus-indexed journals were analysed to uncover key trends, contributors, and research themes within this rapidly evolving field. The analysis identifies “sustainable development” and “China” as well-developed and central motor themes, while “fintech,” “financial inclusion,” and “electronic money” remain central yet still developing areas of inquiry. Notably, Ozili P.K. emerged as the most influential author, with a high citation impact and H-index, followed by Banna H and Mhlanga D. The Journal of Risk and Financial Management, Sustainability (Switzerland), and Finance Research Letters were the leading publication outlets. Geographically, India led in terms of publication volume, reflecting its dynamic fintech ecosystem, whereas the UK and US showed strong international research collaborations. Despite a solid foundation, the literature reveals underdeveloped focus areas, particularly regarding the traditional financial system and the integration of emerging technologies. These points point to meaningful gaps for future exploration, including the application of blockchain, artificial intelligence, and digital identity frameworks to promote inclusive finance. Additionally, socio-cultural factors influencing fintech adoption remain insufficiently explored, especially in underserved communities. Cross-country comparative research and long-term studies are also needed to deepen our understanding of fintech’s role in achieving inclusive and sustainable economic growth.
Cultural Persistence in the Architecture of Sa’o Nggua: A Case Study of Traditional Lio Settlements in Nggela, Flores Island Mukhtar, Mukhlis A.; Antariksa, A; Wulandari, Lisa Dwi
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.v5i4.1160

Abstract

This study investigates the persistence of cultural meaning in the traditional architecture of Sa’o Nggua within the Nggela settlement of the Lio ethnic group in Flores, Indonesia. Through a structuralist and ethnographic approach, the research analyzes spatial patterns, architectural typologies, and ritual calendars at both macro (village) and micro (house-cluster) scales. Findings reveal that the settlement exhibits a sectoral-concentric spatial configuration aligned with cosmological beliefs, where sacred–profane binaries structure both space and function. Despite modern pressures and environmental disruptions, architectural forms remain consistent—particularly the tripartite interior and symbolic roof—due to ritual obligations and local material abundance. Twelve annual rites form an eco-ritual feedback loop, ensuring house maintenance and agricultural productivity. The coexistence of Catholic worship and ancestral practices demonstrates a layered cultural resilience. Limitations include a lack of spatial quantification and gendered labor metrics. Future research should explore UAV-based mapping, longitudinal ethnography, and climate-adaptive potentials of indigenous construction techniques. Overall, Sa’o Nggua functions not merely as a shelter but as a living symbol of cultural continuity, ecological adaptation, and social cohesion in the face of change. This underscores the relevance of vernacular architecture as a model for sustainable and resilient built environments.
Adaptive Beamforming Techniques for Mmwave and Thz Communications In 6G Kundra, Danish; Vamalatha, Bodireddy; Jadhav, Yogesh; Leo, L. Megalan; Hota, Sarbeswar; Sunil, M.P.; Agarwal, Trapty
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.v5i1.1489

Abstract

The transition to sixth-generation (6G) wireless networks focuses on fulfilling extraordinary demands for ultra-high data rates, extensive connectivity, and ultra-low latency.  Achieving these goals requires extensive spectrum resources, particularly in the emerging millimeter-wave (mmWave) and terahertz (THz) frequency bands. Unfortunately, reliable communication at these frequencies is greatly hindered by high path loss, molecular absorption, blockage, and, even more so, the growing susceptibility to loss of line of sight. To combat these issues, path adaptive beamforming methods are critical in targeting narrow beams to improve link reliability. This work focuses on complete coverage of adaptive beamforming methods applied to 6G mmWave and THz communications, covering all forms of beamforming from fully analog to hybrid and digital architectures. This also includes recent machine learning advancements in beam alignment optimization, channel estimation, user tracking, and overhead minimization. Further, the paper details these systems' performance, complexity, and energy-efficient trade-off factors while putting forth open research opportunities towards developing intelligent, resilient, and adaptive beamforming techniques for next-generation wireless systems.
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.
Digital Teaching and Learning Scaffolding in Education: A Systematic Review Using Bibliometric Analysis Safahi, Luthpi; Mulyono, Herri; Akbar, Budhi; Busthami Nur, Abdul Hamid; El Khuluqo, Ihsana
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.v5i4.1465

Abstract

This study aims to systematically analyze the evolution, research trends, and scientific impact of digital teaching and learning scaffolding through the application of bibliometric analysis. By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, a total of 396 relevant journal articles published between 2013 and 2023 were identified from Google Scholar using Publish or Perish (PoP). The dataset was then analyzed using VOSviewer, with a specific focus on citation patterns, keyword co-occurrence, co-authorship networks, and thematic clusters. The bibliometric mapping revealed five dominant research clusters: (1) integration of digital technology in higher education, (2) learning strategies and their impact on students’ outcomes, (3) digital games and scaffolding approaches in children’s education, (4) teacher education development and practice, and (5) contextual influences in educational research. In addition, the study identifies prominent authors, highly influential publications, and major institutional as well as international collaborations, offering a comprehensive overview of the intellectual structure of the field. The findings highlight an increasing global interest in digital scaffolding in education, demonstrating its significant contributions to improving student engagement, cognitive development, problem-solving, and personalized learning experiences. Nevertheless, several challenges remain evident, particularly in terms of accessibility across diverse contexts, teacher readiness to adopt innovative pedagogies, and technological limitations within resource-constrained environments. This research contributes to the academic discourse by providing evidence-based insights and practical recommendations to optimize the implementation of digital scaffolding in contemporary educational settings. Moreover, it emphasizes the importance of continuous exploration of emerging technologies such as artificial intelligence (AI), virtual reality (VR), and adaptive learning systems to further strengthen the effectiveness and sustainability of digital scaffolding practices in the future.
An Efficient Hybrid Model-Based Classification of Online Personalized Ad and User Intent Detection Using CNN and Deep Q-Networks Thunuguntla, Satish Babu; S, Murugaanandam; R, Pitchai
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.v5i4.1242

Abstract

As a vast market, online advertising has promptly gained aggregate interest in a vast array of platforms, including mobile apps, search engines, third-party websites, and social media. In online marketing, the success of online campaigns is a challenge, which is often assessed by user response using several measures, such as product subscriptions, explicit customer feedback, clicks on ad creatives, or transactions obtained through online surveys. Auditing advertising images, or "creatives," prior to their appearance on publishers' websites is one of the most crucial quality control steps in online advertising. This ensures that advertisements only appear on websites that are appropriate for them. The user experience, the publisher's reputation, and maybe legal ramifications can all be negatively impacted if a sensitive creative is shown on the incorrect website. To detect and classify whether an advertisement has any sensitive content, we use a machine learning algorithm to process the creative image and combine this with the historical distribution of sensitive categories linked to the creative's landing page. To protect against this, the study presents an efficient hybrid model using CNN and Deep Q-networks for the classification of online personalized ads and user intent detection. Initially, the HCOAUICNN-DQN model uses data preprocessing to preprocess the input dataset from the online advertisement website. Next, extract those input features through the pretrained CNN-feature maps. Following, DQN is applied for the user intent detection classification of and online personalized ads. Finally, the hyperparameter tuning using Optuna optimization algorithm is applied to improve the real-time classification performance. A set of experiments was conducted to analyze model efficiency using different datasets of advertising images. The performance of the HCOAUICNN-DQN model will be assessed through accuracy, F1-score, recall, and precision metrics. The HCOAUICNN-DQN method obtains superior outcomes when compared to other existing approaches.
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.
Amanu: CNN-RNN Kapampangan Language Learning-Based System for Grade School Learners Mangune, John Elmo Gozun; Mallari, Marvin Ordoñez; Pinpin, Arzel Paguio
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.v5i4.992

Abstract

This study examines the development and implementation of "Amanu", a deep learning-based computer system designed to enhance Kapampangan language acquisition among grade school learners. Utilizing a CNN-RNN architecture, the system addresses challenges in preserving and promoting the Kapampangan language. The research employed a quantitative descriptive approach, using ISO 25010-based surveys for data collection. Developed using Agile methodology, Amanu incorporates features such as a pronunciation checker, multimedia lessons, interactive games, and a comprehensive dictionary. Findings indicate that Amanu significantly aids both teachers and learners in Kapampangan language education, receiving high ratings across all ISO 25010 categories with an overall mean score of 3.80. The study concludes that integrating such systems into standard teaching methods can revolutionize language learning approaches, making Kapampangan acquisition more accessible and engaging. Recommendations include incorporating adaptive learning algorithms and expanding cultural content. This research contributes to the fields of educational technology and language preservation, demonstrating the potential of deep learning-based systems in supporting the education of endangered languages.
Sustainable Supply Chain Practices in Engineering-Based Manufacturing Firms Nuritdinovich, Muhidinov Ayubbek; Vij, Priya
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.v5i2.1494

Abstract

Sustainable Supply Chain Management (SSCM) assesses the environmental implications associated with all conventional supply chain (SC)activities to mitigate their adverse effects. This study presents a fuzzy-based methodology for examining obstacles in SSCM within the environment. Seven manufacturing companies from the electronics industry are participating. The study's findings reveal three primary challenges in engineering-based manufacturing firms. The barriers include knowledge-related factors (insufficient understanding of the adverse effects on business, absence of training programs for industry-specific training, monitoring, and mentoring, lack of technical expertise, and challenges in recognizing environmental possibilities), commitment-related issues (deficiency in corporate social accountability), and design-related challenges (complexities in designing for the reusing/recycling of used goods).The suggested research is among the first investigations undertaken within the environment regarding identifying SSCM barriers in the electrical and electronics industry. Secondly, the obstacles are examined via causation and prominent relationships, which assist decision-makers, policy developers, and organizational managers tackle the essential factors necessary to achieve SSCM activities.
The Miracle of Cryptocurrency: Opportunity at Global Transaction Management, Future Market and Present Value Reality Juanda, Reza; Falahuddin, Falahuddin; Muttaqien, Muttaqien; Ilham, Rico Nur; Ramansyah, Frengki Putra; Multazam, Muhammad
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.v5i1.1111

Abstract

Cryptocurrency is an investment commodity that can generate returns and already has a license to trade in exchange. This study aims to examine the prospects of digital cryptocurrency assets more deeply by summarising the results of literature studies in various countries. In each of the continents scattered in the world, some countries support the existence of cryptocurrency as an investment asset and a means of payment. As a result of this study, it is known that many countries whose governments make strict regulations on support for legality and allow cryptocurrency transactions include: European Union members, namely Germany and Italy, and non-member countries of the European Union, such as Gibraltar. Furthermore, in the Americas, there are Canada and Venezuela. In East Asia and the Asia Pacific, Australia and Japan support the existence of cryptocurrency. Meanwhile, in Southeast Asian countries, there are contradictions between several countries, including Indonesia, Malaysia, Vietnam, and the Philippines, that reject cryptocurrency transactions because they are considered threats to money laundering and the problem of terrorism. However, Singapore and Thailand will protect cryptocurrency investors in their countries and ensure legal regulations for cryptocurrency transactions as investment assets and legal means of payment transactions. Investments in digital currencies or cryptocurrencies are increasingly prevalent worldwide and are supported by significant price increases. Of course, this is a prospect that cryptocurrency transactions can meet the expectations of all users in the world by making regulations regarding the legality of cryptocurrency, so that the transaction model can be integrated between users, both as an asset and a substitute for international payment currencies.

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