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Abdul Hafid Hasim
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INDONESIA
International Journal of Environment, Engineering, and Education
Published by Three E Science Institute
ISSN : -     EISSN : 26568039     DOI : https://doi.org/10.55151/ijeedu
Core Subject : Science, Education, Social, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science
The International Journal of Environment, Engineering, and Education [e-ISSN: 2656-8039] is a peer-reviewed, open-access journal that is published three times a year [in April, August, and December]; this journal provides the right platform for authors to update their knowledge, information, and share their research results with the more significant scientific community publishing research articles explaining the ecological, technical, and educational impact of research from various disciplines publishing research articles explaining the environmental, technical, and educational implications of research from multiple disciplines publishing research As an interdisciplinary scientific publication, this journal encourages collaboration between researchers, academics, practitioners, and policymakers in various sectors to develop sustainable solutions to address environmental, engineering, and educational problems and promote sustainable development.
Arjuna Subject : Umum - Umum
Articles 104 Documents
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Compact Bi-slot Patch Antenna with Tapered Edges for Ka-Band Applications Featuring Machine Learning-Assisted Performance Prediction Raj J, Josiah Samuel; Gopalan, Anitha
International Journal of Environment, Engineering and Education Vol. 7 No. 3 (2025)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v7i3.326

Abstract

Microstrip patch antennas are vital for Ka-band communication owing to their compact size and high performance. This study introduces a modified patch design at 28 GHz featuring two corner truncations and dual-slot integration to enhance impedance matching and broaden the operational bandwidth. The objective of this work is to investigate whether geometrical modifications combined with intelligent modelling can yield improved performance metrics while accelerating the performance evaluation phase through a data-driven surrogate model. The proposed antenna was developed through parametric optimization in Ansys HFSS, in which its structure was systematically varied to achieve stable resonance and improved radiation performance. The optimized prototype achieves a simulated return loss of −67.11 dB, a bandwidth of 3.8 GHz, a VSWR of 1.0009, a peak gain of 7.65 dB, and an input impedance of 50.01 Ω, all indicating strong simulated electromagnetic performance. The design demonstrates a deep resonance corresponding to a high quality (Q) factor, making it a suitable candidate for applications where precise frequency selectivity is paramount. To accelerate evaluation, a machine learning framework was integrated, using 65,682 simulated samples to train regression models for predicting return loss. Among the tested algorithms, the Random Forest Regressor demonstrated the highest accuracy with a mean absolute error of 0.0471 dB and an R² of 0.9995. The integration of electromagnetic simulation and ML-assisted performance prediction demonstrates a reliable pathway for rapid evaluation of Ka-band antennas, offering strong potential for next-generation satellite and wireless communication systems.
Benchmarking Transformer Models Against Classical Approaches for Fake Review Detection on the Deceptive Opinion Spam Corpus Lokeshwaran, K.; Komal Kumar, N.; Senthil Murugan, J.; Elanangai, V.; Sathya, S.
International Journal of Environment, Engineering and Education Vol. 7 No. 3 (2025)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v7i3.334

Abstract

In today’s digital environment, online reviews have become one of the key factors that influence the decisions of customers. This is especially true in areas such as e-commerce, travel and the hospitality industry, where buyers depend heavily on the shared experiences of others before making a choice. At the same time, the growing issue of fake or fabricated reviews has raised serious concerns, as it reduces the reliability of online platforms and creates confusion for consumers. Detecting such misleading reviews is not an easy task, since the language used in them is often very close to what is seen in genuine opinions. In the present work, an attempt has been made to compare the performance of traditional machine learning techniques with that of transformer-based deep learning models for the identification of fake reviews. As part of the baseline, Logistic Regression and Linear SVM were applied with TF-IDF features. On the other hand, advanced architectures like BERT, RoBERTa and XLNet were fine-tuned on the Deceptive Opinion Spam Corpus. The results clearly indicated that the classical models gave accuracies in the range of mid-80 percent, whereas the transformer-based models performed much better, crossing or coming close to 90 percent. Among the transformer models, RoBERTa showed the most balanced performance across precision and recall, XLNet gave the highest recall, which is very important when sensitivity is the main concern, while BERT achieved competitive results with less demand on computing resources.
Mapping the Intellectual Core of Technology Adoption in Digital Startups: A Bibliometric Analysis via Bibliographic Coupling and Co‑Word Networks Rosalin, Sovia; Raharjo, Kusdi; Utami, Hamidah Nayati; Prasetya, Arik
International Journal of Environment, Engineering and Education Vol. 7 No. 3 (2025)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v7i3.355

Abstract

Digital startups are reshaping markets through the use of AI, cloud computing, and blockchain; however, scholarship on how these firms adopt technology remains fragmented. This study systematically maps the intellectual structure and thematic fronts of research on technology adoption in digital startups. A field-tagged Scopus search conducted in September 2025 (coverage 2000–2025) was cleaned and harmonized using a VOSviewer. After de-duplication, 2,243 documents were analyzed via bibliographic coupling (knowledge structure) and co-word analysis (thematic). Four coherent clusters emerge. Strategic innovation and leadership function as the governance backbone that shapes adoption decisions and risk appetite. Sustainable, data-driven business models translate adoption into performance outcomes through analytics capability and value capture. Corporate entrepreneurship within innovation ecosystems bridges firm-level capability with external partners, investors, and accelerators, linking adoption speed to ecosystem embeddedness. Digital business transformation operationalizes AI/cloud investments into processes and customer journeys. Cross-cutting co-word foci, such as perceived usefulness/user experience and organizational readiness, act as mechanisms connecting individual cognition with organizational capability. Emergent topics in policy, regulation, and platform governance appear as boundary conditions that enable or constrain adoption trajectories. The mapping provides an integrative lens organized along two axes: cognitive evaluation and organizational capability that jointly explain adoption in digital startups. It identifies gaps in external enablers and capability maturation paths. A forward-looking agenda is proposed, featuring multi-level models that link cognition, capability, and growth, as well as quasi-experimental evaluations of interface simplification and onboarding, cross-country comparisons of regulatory regimes, and longitudinal tracking of platform transitions.
Achieving Sustainable Coastal–marine Conservation: Lessons from a Community Social Movement in Torosiaje Ecotourism Village, Indonesia Hendra, Hendra; Sumarmi, Sumarmi; Astina, I Komang; Aisyah, Siti; Rijal S, Ahmad Syamsu
International Journal of Environment, Engineering and Education Vol. 7 No. 3 (2025)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v7i3.359

Abstract

The failure of top-down conservation in natural resource management continues to provoke resistance led by local communities. This study analyzes how the coastal community of Torosiaje constructs a polycentric governance system through collective action in response to ecological crises and to the state's appropriation of living space, aiming to achieve blue justice in the management of marine and coastal resources. The complex, polycentric governance in joint management involves various actors, including the state, local communities, and the private sector, who collectively play active roles in decision-making for sustainability. Meanwhile, blue justice requires the fair distribution of natural resources and ecosystem benefits, which is pursued through the collective struggle of the community against ecological injustice. Using social movement and political ecology theories as an analytical framework, this research redefines Community-Based Natural Resource Management (CBNRM) as a more inclusive and responsive model to local dynamics. A qualitative case study design was employed through in-depth interviews, participant observation, and document analysis, which were subsequently analyzed thematically. The findings reveal that integrating local knowledge and formal rules, embodied in the paddakuang and sipakullong conservation groups, results in a more adaptive and just CBNRM model in response to resistance. Cross-village collaboration, participatory ecotourism, and culture-based education strengthen the socio-ecological dimensions of this polycentric governance. This study contributes theoretically by applying social movement theory to redefine successful CBNRM. It argues that sustainable governance is a political outcome shaped by community resistance to ecological injustice and state dispossession, rather than merely a technical model.

Page 11 of 11 | Total Record : 104

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