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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 113 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.
Predictive Modeling of NaOCl Dosage for Iron Removal in a Combined Aeration–Oxidation System Using Gene Expression Programming Alsaeed, Ruba Dahham; Aljaddou, Heba; Shehab, Diala; Alaji, Bassam; Salloum, Durgam
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.235

Abstract

Iron is one of the most prevalent groundwater contaminants and can cause significant aesthetic, operational, and infrastructural problems when present at elevated concentrations. This study aims to (i) experimentally evaluate the effects of pH, dissolved oxygen (DO), and sodium hypochlorite (NaOCl) dosage on iron removal efficiency, and (ii) develop an interpretable Gene Expression Programming (GEP) model to predict the optimal NaOCl dose under varying water-quality conditions. Laboratory jar test experiments demonstrated that iron oxidation is strongly pH-dependent, with maximum removal efficiency (up to 99%) achieved under acidic conditions (pH 4) at a NaOCl dose of 6 mg/L due to hypochlorous acid predominance. Under practical near-neutral conditions relevant to drinking-water treatment (pH 6.5–7.5), aeration alone enhanced iron removal as DO increased, although diminishing returns were observed beyond 6 mg/L DO because of increased energy demand. A combined treatment strategy involving low-dose pre-chlorination followed by aeration exhibited a clear synergistic effect, achieving iron removal efficiencies of approximately 85–89% using NaOCl doses of 1–3 mg/L and DO levels of 4–5 mg/L. This approach reduced overall operational costs by approximately 40% compared with aeration-only treatment. The developed GEP model showed strong predictive performance (R² = 0.94; RMSE = 0.34 mg/L) and generated explicit mathematical expressions linking oxidant demand to pH, DO, and influent iron concentration. Overall, the study confirms the technical and economic advantages of pre-chlorination combined with aeration and highlights the potential of GEP as a transparent decision-support tool for optimizing groundwater iron removal.
A Polya-Aligned Prompting Protocol for ChatGPT Scaffolding: Evidence from Eighth-Grade Systems-of-Linear-Equations Problem Solving Malik, Marwati Abd.; Lince, Ranak; Husnaeni, Husnaeni
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.331

Abstract

Generative artificial intelligence offers new opportunities to scaffold students’ mathematical reasoning, yet rigorous evidence of its impact on secondary students’ problem-solving remains limited. This study examined whether ChatGPT-driven adaptive learning improves eighth-grade students’ problem-solving performance on systems of linear equations in two variables (SPLDV) compared with conventional instruction. A pre–post-test control-group design was implemented with 47 eighth-grade students in Parepare, Indonesia (experimental n = 24; control n = 23) during the 2024/2025 academic year. The experimental group used ChatGPT as an adaptive tutor aligned with Polya’s stages (understand, plan, execute, look back) through guided prompts, hints, and feedback. In contrast, the control group received a lecture and practice. Students completed a six-item contextual SPLDV test scored with a Polya-based rubric. Between-group differences were tested on post-test scores and normalized gains after verifying normality and homogeneity assumptions. The experimental group achieved higher post-test performance (M = 68.33) than the control group (M = 59.57), with a significant difference (p = 0.019; η² = 0.117). Learning gains were also larger in the experimental group (mean N-gain = 0.34, medium) than in the control group (0.21, low; p = 0.001; η² = 0.372). Indicator-level patterns suggested the greatest improvements in understanding the problem and carrying out the plan, whereas devising a plan remained the most challenging stage in both groups. These findings indicate that ChatGPT-based adaptive scaffolding can enhance students’ mathematical problem-solving on SPLDV, but explicit teacher-guided routines are needed to strengthen strategic planning and the critical evaluation of AI outputs.
Engineering Design and Prototyping of an Automated Shuttlecock Feeder with Programmable Court-Zone Targeting Punithan, P. E.; Ramakrishnan, R.; Nallavan, G.; Jayanarasimhan, K.
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.335

Abstract

Manual multi-shuttle badminton drills can introduce variability in feeding rate and placement, reducing training standardization and complicating objective evaluation. Automated, programmable feeding with zone-based metrics can address this limitation. To design and validate a low-cost, automatic nine-shot shuttlecock feeder that delivers shuttlecocks to predefined court zones with controllable speed, direction, and timing. The prototype combined a wooden frame with 3D-printed dropper/ejector components and a dual-wheel launcher fixed at 30°. An ESP32 coordinated two DC motors (launch wheels) and three servomotors (dropper, ejector, and horizontal aiming). Nine-shot programs targeted a 3×3 court grid (left/center/right × front/mid/rear). The feeder was mounted 1.10 m above an indoor regulation court and 1.30 m from the net. For each zone, 12 feather shuttlecocks were launched (108 trials). Dual-camera video (60–120 fps) captured trajectory and top-view landings; Dartfish tagging and planar-homograph calibration converted pixel coordinates to court distances (mean spatial error <3%). All nine programs were executed successfully and produced distinct zone-specific landing distributions. Landing-distance variability was low (coefficient of variation <12% across programs), indicating strong repeatability under fixed settings; rear-court programs showed longer mean distances with similarly tight dispersion. Feeding reliability was 100% across 108 launches, with no blocking, double-feeding, or missed shots. Flight time and estimated near-field launch speed changed consistently with the programmed motor settings. The proposed feeder enables repeatable, structured multi-shuttle training and provides a practical framework for quantifying zone-delivery performance, with future work directed toward refining closed-loop targeting.
Exploring Energy Efficiency and User Attitudes toward Green Energy Implementation in University Buildings Syah, Nurhasan; Haq, Syaiful; Ashar, Faisal; Arbi, Yaumal
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.362

Abstract

The global push for sustainable development has intensified the need to improve energy efficiency in higher education buildings, particularly in hot–humid tropical climates where cooling demand dominates electricity use. This study examines how occupant perceptions, environmental attitudes, and energy-related behaviors relate to measured building energy performance in a tropical university building, using a convergent parallel mixed-methods design. An ASHRAE Level 1–based energy audit (aligned with Indonesia’s MEMR Regulation No. 13/2012) profiled electricity consumption by end-use systems and was complemented by a 38-item questionnaire and semi-structured interviews with students, lecturers, and administrative staff. The audit estimated total annual electricity consumption of 366,897.7 kWh/year, corresponding to an average Energy Use Intensity (EUI) of 17.34 kWh/m²/month, and associated emissions of 285,108.85 kgCO₂eq. Cooling/HVAC accounted for the largest share of electricity use (≈55%), followed by plug loads/equipment and lighting. Survey results indicated generally high pro-environmental attitudes; however, quantitative associations between aggregated floor-level perceptions/behaviors and electricity use were exploratory, given the limited number of analytic units (four floors/zones). Still, floor-level correlations consistently suggested negative relationships between behavioral variables and energy consumption, with expectations toward green-energy practices showing a particularly strong inverse association (r = –0.968). Qualitative findings highlighted practical operational and behavioral drivers such as temperature setpoints, schedule discipline, and equipment shutdown practices, pointing to actionable opportunities for demand reduction. Overall, the study contributes an integrated audit–behavior perspective to support occupant-centered interventions, green-campus policy alignment, and sustainability-oriented learning activities for long-term low-carbon campus development in hot–humid contexts.
Perceived Security and Trust as Mechanisms of P2P Adoption Technology: Evidence from Pre-Adopters Using PLS-SEM Approach Wijaya, Indra Dharma; Salam, Rudi; Ghozi, Saiful; Mubarok, Faiz Ushbah; Subkhan, Muhamad Fajar
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.373

Abstract

Peer-to-peer (P2P) lending can expand working-capital access for micro-entrepreneurs, yet borrowing via digital platforms heightens perceived vulnerability due to sensitive data disclosure and binding repayment obligations. This study examines how perceived security and trust shape adoption intention in a high-stakes fintech context by extending the Technology Acceptance Model (TAM) and testing whether security and trust transmit the effect of perceived usefulness to intention. Using an explanatory cross-sectional design, we collected offline-administered survey data from 204 Indonesian micro-entrepreneurs who had not previously adopted P2P lending to capture pre-adopter perceptions better and reduce digital-selection bias. The model was estimated using PLS-SEM 3. The results indicate that all hypothesized relationships are positive and statistically significant. Perceived usefulness significantly enhances perceived security (β = 0.562) and trust (β = 0.259), while perceived security exerts a strong positive effect on trust (β = 0.517). Intention to use P2P lending is directly influenced by perceived security (β = 0.337), trust (β = 0.213), and perceived usefulness (β = 0.208). Mediation analysis confirms that perceived security (β = 0.189) and trust (β = 0.055) partially mediate the effect of perceived usefulness on intention to use. The model explains 42.4% of the variance in intention to use (R-squared = 0.424) and demonstrates adequate predictive relevance (Q-squared = 0.263). Overall, perceived security emerges as the most influential determinant of adoption intention, underscoring the importance of security-by-design features, transparent governance, and robust consumer-protection frameworks in fostering trust and accelerating P2P lending adoption among micro-entrepreneurs.
Technology-Supported Student-Centered Science Learning and Digital Competence Development in Upper Secondary Classrooms Nhi, Vo Doan Yen; Long, Le Thai Minh
International Journal of Environment, Engineering and Education Vol. 8 No. 1 (2026)
Publisher : Three E Science Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55151/ijeedu.v8i1.426

Abstract

Digital competence is increasingly recognized as a core outcome of science education, yet evidence remains limited. This study examined whether technology-supported science instruction was associated with stronger multidimensional digital competence than conventional instruction and explored how seven competence dimensions were structurally related. A quasi-experimental pretest-posttest non-equivalent control group design was conducted with 180 Grade 11 students from four intact classes in two public schools. Over eight weeks, the experimental group engaged in inquiry- and project-based digital science learning, whereas the comparison group received instruction. Digital competence was assessed using a 28-item questionnaire and seven performance-based tasks. The experimental group showed larger gains across all dimensions, with significant baseline-adjusted differences across all outcomes (all p < 0.001) and moderate-to-large effects (partial η² = 0.26-0.29). More importantly, the pattern suggests that technology-supported student-centered science learning is associated not only with stronger technical performance but also with an integrated competence profile spanning information handling, data interpretation, communication, collaboration, problem-solving, creativity, and operational fluency. The structural findings further suggest that information literacy and digital communication may function as foundational competencies supporting analytical, collaborative, technical, and creative performance, with the strongest pathway from Digital Communication Skills to Collaborative Technology Use (β = 0.58). These findings imply that digital competence in science is best fostered when digital tools are embedded in inquiry, communication, collaboration, and production tasks rather than taught as isolated technical skills. Because the study involved only four intact classes, the findings should be interpreted as comparative and exploratory rather than definitive causal evidence.

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