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30,907 Article Results

An artificial neural network-based decision support model for early prediction of mathematics learning challenges using the CRISP DM framework

10.11591/ijeecs.v43.i2.pp618-627
Harry Dhika , Surajiyo Surajiyo , Lasia Agustina , Abdul Muchlis
Identifying mathematics learning difficulties remains a challenge for educators due to the subjectivity and inefficiency of conventional methods in capturing psychological factors. To address this limitation, this study proposes an artificial neural network (ANN)-based decision support model developed within the cross-industry standard process for data mining (CRISP-DM) framework. The model integrates 16 academic indicators (quizzes, exams, remedial frequency, online activity) and psychological factors (motivation, anxiety, self-confidence, interest) from 163 student records at SMA Muhammadiyah 16 Jakarta. Synthetic minority over-sampling technique (SMOTE) and focal loss are applied to handle class imbalance and improve reliability. The proposed model achieves 98% accuracy and a 0.97 F1-score in classifying students into three difficulty levels: Easy, moderate, and difficult. These findings demonstrate the model’s effectiveness in capturing complex relationships between cognitive and affective features. Unlike prior studies that rely solely on academic performance, this work contributes a robust, comprehensive data-driven framework that enhances multiclass classification for early educational intervention within the Indonesian context.
Volume: 43
Issue: 2
Page: 618-627
Publish at: 2026-08-01

Low-power high-speed FinFET DRAM array using sleep transistors

10.11591/ijeecs.v43.i2.pp413-424
N Praveena , N Shylashree
Dynamic random-access memory (DRAM) is a fundamental memory technology widely employed in modern digital systems because of its high storage density and simple cell structure. However, conventional DRAM cells suffer from considerable power dissipation and propagation delay, which limit their suitability for high-speed and low-power applications. This paper presents three novel FinFET-based DRAM architectures incorporating sleep transistor techniques to reduce power consumption while improving operating speed. The proposed designs include a 2T-DRAM with sleep transistors and two configurations of 3T-DRAM with sleep transistors. The FinFET technology enhances switching performance and reduces propagation delay, whereas the sleep transistor technique effectively suppresses leakage, dynamic, and short-circuit power during memory operations. The proposed DRAM cells are designed and evaluated using the cadence virtuoso analog design environment. Simulation results demonstrate significant improvements over conventional DRAM architectures. The proposed 2T-DRAM achieves a 66% reduction in write delay, while the proposed 3T-DRAM achieves up to a 98% reduction in read delay. Furthermore, write power consumption is reduced by 56.8%, 63.02%, and 99.6% for the 2T, 3T-B, and 3T-C configurations, respectively. During read operations, power consumption is reduced by 99.8%, 99.5%, and 99.8%, respectively. These results demonstrate that the proposed FinFET DRAM architectures provide an effective solution for high-speed, low-power embedded memory applications.
Volume: 43
Issue: 2
Page: 413-424
Publish at: 2026-08-01

Transformer-based sentiment modeling for identifying cross country fintech perception gaps

10.11591/ijeecs.v43.i2.pp522-533
Kayla Zhafira Ardinov , Muhardi Saputra , Riska Yanu Fa’rifah
Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.
Volume: 43
Issue: 2
Page: 522-533
Publish at: 2026-08-01

A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/44980
Raden Sutiadi , Sparisoma Viridi , Giyanto Giyanto , Andarta F. Khoir , Rizkie S. Utama , Elsa D. Aulia , Tri A. Hadi , Ludi Parwadani Aji
Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
Volume: 43
Issue: 2
Page: 628-639
Publish at: 2026-08-01

Immersive technology in English language learning: a bibliometric analysis

10.11591/ijere.v15i4.37947
Zhou Bo , Lim Seong Pek , Nahdia Kabir , Mohamed Bouteraa , Asna Asna
This bibliometric analysis, drawing on data from the Web of Science (WoS) core collection, explores the expanding role of immersive technologies in English language education. Virtual reality (VR) and augmented reality (AR) have shown strong potential to improve learner motivation, engagement, and communicative competence, yet their integration into formal English language settings remains uneven. By analyzing 248 peer-reviewed articles published between 2021 and 2025, this study finds significant trends, influential contributors, and emerging areas of interest within the field. The findings show a steady increase in publications and citations, reflecting growing recognition of the educational value of immersive environments. Prominent themes include emotional engagement, lowered language anxiety, and improved performance in vocabulary, speaking, listening, and cultural understanding. Much of the literature underlines authentic and situated learning, VR-based interactive environments, VR-supported problem-based learning, and AR-assisted vocabulary development. The analysis also identifies leading countries, with China and the United States producing the largest share of research, a pattern supported by strong institutional participation worldwide. These insights help guide educators and policymakers as they consider how to bring immersive technologies into English instruction. The study also establishes a foundation for future research on effective, engaging, and sustainable immersive language learning practices. Overall, these findings clarify how research on immersive technologies in English language education has evolved between 2021 and 2025 and identify influential studies and key contributors. They also point to persisting gaps, such as equity, teacher readiness, and cognitive-load–informed design, that warrant further investigation.
Volume: 15
Issue: 4
Page: 3623-3635
Publish at: 2026-08-01

Computational framework for smart tourism management: hybrid time series decomposition and predictive modeling

10.11591/ijai.v15.i4.pp3421-3430
Iwan Ady Prabowo , Hendro Wijayanto , Teguh Susyanto
Smart tourism management in rural multi-destination settings requires forecasting methods that are accurate enough to support visitor allocation, infrastructure readiness, and ecological protection. This study presents a decomposition-based forecasting framework for Sidowayah Village, Central Java, Indonesia, which integrates three attractions with different demand profiles: Umbul Manten, Siblarak, and Kampung Dolanan. Using monthly visitation data from May 2023 to April 2024, the study compares additive and multiplicative decomposition models within a common workflow of data collection, preprocessing, trend-seasonal decomposition, model evaluation, and sustainability-oriented interpretation. The contribution of the study lies in clarifying destination-specific criteria for selecting additive versus multiplicative models, improving methodological transparency in preprocessing and temporal validation, and translating forecast outputs into practical smart tourism actions aligned with sustainable development goals (SDGs) 11 and 12. The results show that the multiplicative-average all model yields the lowest mean absolute percentage error (MAPE) for Umbul Manten (14.1%) and Siblarak (56.8%), while the additive-centered moving average model is more suitable for Kampung Dolanan based on mean absolute deviation (MAD) (162.6). Although the 12-month dataset limits long-term generalization, the framework provides a reproducible basis for data-informed tourism management in rural destinations.
Volume: 15
Issue: 4
Page: 3421-3430
Publish at: 2026-08-01

Video summarization using deep image captioning models

10.11591/ijeecs.v43.i2.pp547-554
Qudes M. B. Aljelawy , Sarah S. Mohammed , Entessar K. Hanoun
This research presents a novel approach for video summarization by leveraging deep image captioning models. A pretrained image captioning model, namely Salesforce's bootstrapped language image pretraining (BLIP), is used to extract keyframes from a video at regular intervals and produce natural language descriptions. These textual descriptions are then filtered for non-repetition and concatenated into a coherent summary, allowing users to understand the video’s content without viewing it in full. The proposed framework aims to improve video browsing, indexing, and retrieval efficiency, particularly for big datasets. The proposed method achieves a significant reduction in redundancy by 40% compared to raw captioning sequences. Evaluation using semantic consistency checks demonstrates that the BLIP-based framework maintains high descriptive accuracy even in complex scenes, providing a scalable solution for large-scale video indexing.
Volume: 43
Issue: 2
Page: 547-554
Publish at: 2026-08-01

Rethinking computer-based examinations in higher education: psychological, technical, and pedagogical challenges from students’ learning experience and future directions

10.11591/ijere.v15i4.39228
Ahmad Adnan AlZyoud , Eman Mohammad Qudah
The swift adoption of computer-based examinations (CBEs) in higher education has revolutionized assessment methods; nonetheless, there is a lack of thorough research investigating the psychological, technical, and pedagogical experiences of students using these systems. This research explores the various challenges associated with CBEs at Yarmouk University and assesses their influence on students’ learning experiences. A cross-sectional quantitative survey was conducted among 545 undergraduate students from various academic fields during the first semester of the 2025–2026 academic year. Both descriptive and inferential analyses were performed to evaluate students’ perceptions. Research shows a moderate level of acceptance for CBEs, but notable worries remain. The main source of stress was found to be technical reliability, often overshadowing worries about educational content. The one-way navigation aspect was recognized as a significant obstacle, which restricted students’ ability to review answers and added to cognitive strain and hasty decision-making. Furthermore, a significant gap in feedback was noted, as students mostly viewed the system as a tool for grading rather than a resource for ongoing learning. The research finds that successful digital assessment necessitates not just operational effectiveness but also adaptable design, alignment with pedagogical goals, and valuable feedback systems. Practical implications involve rethinking navigation elements, improving technical support, and offering focused training for faculty.
Volume: 15
Issue: 4
Page: 2861-2873
Publish at: 2026-08-01

English medium instruction in Jordanian medical education: a systematic review

10.11591/ijere.v15i4.39643
Hassan Mohammad Bani-Issa , Norsofiah Abu Bakar , Muhammad Zaid Daud , Jong Hui Ying , Hytham M. Bany Issa
English-medium instruction (EMI) dominates Jordanian medical education, yet its equity implications and consequences for assessment validity remain poorly evidenced, particularly after the disruptions of the COVID-19 pandemic. Following preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, this review searched Scopus, Web of Science, ERIC, and PubMed for peer-reviewed work published between 2000 and 2024. A total of 34 studies met inclusion criteria after mixed methods appraisal tool (MMAT) quality appraisal and were analyzed through thematic synthesis. The evidence reveals a structural paradox: EMI supports international academic integration while disadvantaging students from Arabic-medium secondary schools by conflating English proficiency with medical competence. The lexical and morphological complexity of medical English increases cognitive demands and became more pronounced during pandemic-related online teaching. Students rely on code-switching and morphological analysis as coping strategies, but neither is recognized in policy or assessment. The review delivers the first PRISMA-aligned synthesis of EMI in Jordanian medical education, proposes the Jordanian Medical English Corpus (JoMEC) as a corpus-based diagnostic for measuring lexical burden, and reframes EMI equity as a measurable issue of assessment validity rather than a normative concern alone. Findings support bilingual scaffolding and validity-oriented assessment reform to advance equity in medical education across Jordan and comparable Middle East and North Africa (MENA) contexts.
Volume: 15
Issue: 4
Page: 3204-3214
Publish at: 2026-08-01

Beyond reductionism: systems thinking for the next generation of electrical and computer engineering

10.12928/telkomnika.v24i4.3776
Tole; Universitas Ahmad Dahlan Sutikno
Classical electrical and computer engineering has achieved remarkable progress through reductionist methodologies that decompose complex systems into manageable, analyzable, and optimizable components. While this paradigm remains indispensable for scientific rigor and engineering design, it is increasingly challenged by contemporary systems characterized by interconnectedness, dynamic interactions, and multi-scale complexity. This editorial argues that future engineering requires extending, rather than replacing, reductionist thinking with systems thinking capable of capturing interdependence, emergence, resilience, and holistic system behaviour. Beyond component-level optimization, engineering must increasingly consider interactions among technological, human, environmental, and societal dimensions that collectively shape system performance and long term sustainability. Systems thinking therefore provides a complementary paradigm for understanding how complex engineering systems adapt, evolve, and generate behaviours that cannot be inferred solely from individual subsystems. This perspective redefines electrical and computer engineering as an integrated socio-technical discipline in which analytical precision is combined with systemic understanding to address increasingly complex real-world challenges. Moving beyond reductionism does not diminish the value of analytical methods but expands their scope within broader interconnected contexts. This paradigm shift establishes the conceptual foundation for the subsequent evolution toward adaptive, human centred, and ultimately responsible engineering.
Volume: 24
Issue: 4
Page: 1083-1090
Publish at: 2026-08-01

Pedagogical environments for the formation of research competence in students within practice-oriented chemistry education

10.11591/ijere.v15i4.39216
Kanat Sadykov , Nesipkhan Bektenov , Nursulu Zhussupbekova , Ainash Baidullayeva
Despite the importance of research competence in modern chemical education, traditional instructional methods often fail to sufficiently prepare students for independent scientific inquiry and professional growth, creating a gap between theoretical knowledge and practical application. The research aimed to experimentally validate a multifaceted system of pedagogical environments (research-focused, metacognitive-digital, and motivational) designed to foster research competence in chemistry students through practice-oriented learning. The research design of the research was a 30-week longitudinal pedagogical experiment utilizing a quasi-experimental design with pre- and post-test assessments. The sample comprised of 325 third- and fourth-year future chemistry teachers (experimental group (EG)=165; control group (CG)=160) from a national university in Kazakhstan. Statistical analysis (Pearson χ² test) confirmed significant growth in the EG across all competence criteria, with the proportion of students reaching “high-level” research proficiency increasing from 10.9% to 38.8%. The key implication is integrating structured pedagogical environments into teacher-training curricula effectively bridges the gap between theoretical knowledge and practical inquiry, providing a scalable framework for modernizing science, technology, engineering and mathematics (STEM) education.
Volume: 15
Issue: 4
Page: 3463-3478
Publish at: 2026-08-01

Evaluating the methodological admissibility of generative AI tools for linear regression in graduate-level research

10.11591/ijere.v15i4.38496
Valery Okulich-Kazarin , Kanat Kozhakhmet
With the growing use of generative artificial intelligence (AI) in academia, a key methodological question concerns the statistical correctness of AI-assisted quantitative analysis. This study empirically evaluates the use of generative AI tools for linear regression in graduate-level research. The authors used a methodological approach in which estimates from four AI systems (ChatGPT 4.0, DeepSeek v3.2, Gemini 3 Pro, and Grok 4.1) were compared with estimates obtained using Microsoft Excel (Windows 10). The analysis was performed on five time series using a fixed prompt structure. Comparability was assessed using thresholds for regression coefficients, the coefficient of determination (R²), and predicted results for 2030. The results show that under controlled conditions and within the ordinary least squares (OLS) method, the AI tools generate statistical results with varying degrees of accuracy. However, deviations in coefficients and predictions highlight the need for systematic validation. The study concludes that AI tools can serve as auxiliary methodological support, provided transparency, reproducibility, and threshold-based verification are ensured in graduate research practice.
Volume: 15
Issue: 4
Page: 2874-2882
Publish at: 2026-08-01

Ethical and cultural perspectives on ChatGPT use in higher education

10.11591/ijere.v15i4.37857
Masroor Alam , Suchi Dubey
Academic writing now faces additional challenges because users cannot identify when they use their human abilities instead of ChatGPT generative artificial intelligence (AI) tool assistance. The rapid emergence of generative AI tools such as ChatGPT has raised important ethical and cultural questions in higher education, particularly concerning authorship, originality, and responsible academic practice. The research obtained 55 open-ended survey responses from students who studied at different levels and pursued various subjects across multiple geographic areas. Among these responses, 23 provided sufficiently detailed reflections and were analyzed for qualitative insights. Using a qualitative research design, the research applied Braun and Clarke’s thematic analysis to identify three connected themes which include students’ ethical perceptions of ChatGPT use, perceived benefits and concerns in academic work, and the role of cultural context in shaping AI acceptance. Students acknowledge the helpful features of ChatGPT but they remain unclear about plagiarism rules and academic authenticity standards and institutional policies. The findings indicate that students generally perceive ChatGPT as a supportive learning tool when used responsibly, while also expressing concerns regarding dependency and unclear institutional guidance. Overall, the study highlights the need for clearer institutional policies and culturally informed AI literacy to support responsible use of generative AI in academic writing.
Volume: 15
Issue: 4
Page: 3576-3582
Publish at: 2026-08-01

Exploring the association among trait resilience, well-being, and coping strategies in Sicilian teenagers

10.11591/ijere.v15i4.37761
Sagone Elisabetta , Indiana Maria Luisa
Resilience is a personality trait strictly influenced by several protective and risk individual factors and analysis of its strengthness is useful to enhance the psychological well-being of teenagers. The aim of this quantitative cross-sectional study was to explore the relationships among resilience in terms of personality trait, psychological well-being, and coping strategies in a large group of Sicilian teenagers. We hypothesized that: the more the teenagers used functional coping strategies (e.g., active and supportive coping strategies), the more they were highly resilient, and they scored higher in dimensions of psychological well-being; the more the teenagers showed high levels of psychological well-being, the more they were highly resilient. The sample consisted of 467 teenagers (age-range: 11–13), 235 girls and 232 boys, randomly recruited from two state junior schools in Catania, Sicily (Southern Italy). For data collection, we used comprehensive inventory of thriving (CIT) for psychological well-being, resilience scale, and children’s coping strategies checklist-R1. Results indicated that there were statistically significant correlations between resilience and dimensions of psychological well-being, as well as between resilience and coping strategies. In addition, multiple regression analyses showed that the use of functional coping strategies and high values of psychological well-being had a positive impact on resilience of teenagers. Future research will compare these findings with those deriving from samples of children to highlight the presence of similarities or differences in the use of coping strategies.
Volume: 15
Issue: 4
Page: 3038-3048
Publish at: 2026-08-01

Determinants of entrepreneurial intentions of private university students in Thailand

10.11591/ijere.v15i4.39455
Nithima Yuenyong , Tanpat Kraiwanit , Ekaphot Congkrarian
The growing interest in student entrepreneurship, research has yet to comprehensively examine both internal and external determinants of startup intentions among private university undergraduates in Thailand, a context shaped by rapid digital transformation and national innovation priorities. This study investigates the factors predicting entrepreneurial intentions among 400 undergraduate students at private universities in Pathum Thani, Thailand. Using a quantitative research design, data were collected via a validated structured questionnaire and analyzed through descriptive statistics and stepwise multiple regression. Five factors significantly predicted startup entrepreneurial intention: financial resources, entrepreneurial skills, prior experience, economic conditions, and technological factors, collectively explaining 60.3% of the variance. Financial resources and entrepreneurial skills emerged as the strongest predictors, while political-legal, socio-cultural, and environmental factors were not significant predictors. The study concludes that entrepreneurial intention is shaped by the interplay between individual competencies and contextual enablers. These findings call for universities to strengthen entrepreneurship education across all faculties and for policymakers to improve students' access to financial resources and startup support mechanisms.
Volume: 15
Issue: 4
Page: 3111-3123
Publish at: 2026-08-01
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