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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 73 Documents
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Search results for , issue "Vol 5, No 4 (2025)" : 73 Documents clear
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.
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.
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.
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.
Building Collaborative Advantage in Hospital Systems: The Role of Supply Chain Collaboration, Innovation, and Digital Transformation in Class C Hospitals in Java Island Uli, Syahdani; Anindita, Rina; Eff, Aprilita Rina Yanti; Syah, Tantri Yanuar Rahmat
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.v5i4.1483

Abstract

This study aims to analyze the influence of transformational leadership, digital transformation, supply chain collaboration, and innovation on collaborative advantage and the performance of type C hospitals in Java, with government subsidies as a moderating variable. Using a quantitative approach and PLS-SEM analysis on 50 hospitals, the results show that most direct relationships between variables are insignificant, except for the influence of innovation on collaborative advantage and the influence of collaborative advantage on hospital performance, which are proven to be significant. In addition, government subsidies only play a significant role in strengthening the relationship between supply chain collaboration and collaborative advantage. These results confirm that collaborative advantage is a key factor in improving hospital performance, with innovation as its main driver. Meanwhile, transformational leadership and digital transformation have not shown a strong direct influence due to bureaucratic limitations and implementation readiness. These findings also indicate that government subsidies are more effective in the early stages of establishing collaborations, rather than directly improving performance. This study highlights the importance of external collaboration strategies and innovation in improving the competitiveness of public hospitals, as well as the importance of adapting global theories to the local context in developing health policies in Indonesia.
Islamic Law in the Era of Artificial Intelligence: A Systematic Literature Review Sudirman, Sudirman; Sutiah, Sutiah; Supriyono, Supriyono
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.v5i4.1072

Abstract

This systematic literature review examines the intersection between Islamic law and artificial intelligence, aiming to identify how Shariah principles address the ethical, legal, and practical challenges posed by emerging AI technologies. The study applies the PRISMA method to analyse 67 peer-reviewed publications from 2010 to 2025 sourced from central databases. The reviewed literature is categorised into five core themes: Shariah-aligned AI ethics, AI personhood and legal responsibility, the integration of AI in Islamic finance and judiciary systems, AI-assisted fatwa issuance and ijtihad, and regulatory gaps in aligning AI with maqasid al-shariah. Findings suggest an increasing scholarly engagement with Islamic jurisprudence as a moral compass for technological governance. However, the research also reveals inconsistencies in theological interpretations and a lack of policy frameworks within Muslim jurisdictions. The review concludes that a cohesive Shariah-based framework for AI ethics is both necessary and timely, and it proposes strategic directions for future research and the development of institutional policy.
A CNN-Driven Image Analysis Approach for Accurate Detection of Plant Leaf Diseases Jha, Suresh Kumar; Misra, Yogesh; Nyayadhish, Renuka; Rawat, Manoj Kumar; K, Kiran Kumar; Neelam, Nagaveni
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.v5i4.1299

Abstract

Plant leaf diseases are a key concern for agriculture and result in significant loss of crop yield and economic losses globally. It is vital to efficiently and accurately detect plant diseases to properly manage crops and control their diseases. This paper demonstrates a CNN-based image analysis model to automatically identify and classify plant leaf diseases from digital images. Deep learning is used in the proposed method to spontaneously learn hierarchical features from original image data, without the use of feature engineering. The model was trained and evaluated on a collection of high-resolution healthy and diseased leaf images collected from different plant species. Preprocessing (normalisation, noise filtering, and contrast increment) and data augmentation (rotations, scale changes, and flips) were also performed on the pre-processed images, and it was expected to achieve good generalisation and reduce overfitting. The CNN architecture was optimised using transfer learning in combination with hyperparameter tuning. Evaluation experiments showed that the framework attained a classification “accuracy of 96.2%, 95.8% precision, 96.5% recall, and 96.1% F1-score”. The model proved to be robust under varying light conditions and complex background settings, demonstrating its real-world applicability. In addition, the model’s lightweight architecture supports mobile and edge computing implementation, enabling real-time and on-site diagnostic capabilities. This method provides an automated, scalable system for plant disease detection, thus enabling early intervention, reducing chemical treatment reliance, and fostering sustainable agricultural practices, fostering environmentally friendly approaches. The results demonstrated the capability of CNN systems towards transforming the plant health monitoring practices in precision agriculture.

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