arXiv subjects

Wiga Maulana Baihaqi

Publications and source records attributed to Wiga Maulana Baihaqi.

3 recordsLinked to original sources

arXiv2026

Beyond Local Power: Functional Connectivity Analysis for Subject-Independent Learning Style Recognition

Identifying individual learning styles optimizes pedagogical efficacy. While traditional questionnaires are structured, behavioral tracking methods require prolonged interaction log accumulation. To overcome these temporal constraints, this paper proposes an objective Electroencephalography (EEG) approach evaluating Phase Locking Value (PLV) connectivity against localized features across the Active-Reflective (AR) and Verbal-Visual (VV) Felder-Silverman dimensions. EEG signals were recorded from 28 participants during Raven's Advanced Progressive Matrices tasks. Support Vector Machine classification used Leave-One-Subject-Out Cross-Validation (LOSO-CV) alongside a 70:30 intra-subject split. The VV dimension achieved 70.00% subject-level accuracy driven by distinct fronto-occipital polarization. Conversely, the AR dimension yielded lower cross-subject generalizability (55.56%) due to overlapping executive networks and a "Systematic Neural Inversion" phenomenon, where stable individual connectivity signatures operated diametrically opposed to global boundaries (up to 20-0 voting margins). Ultimately, these outcomes demonstrate that rigid "one-size-fits-all" classifiers are bounded by biological diversity, emphasizing the need for future adaptive feature transformation techniques to bridge the cross-subject generalization gap.

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arXiv2026

From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems

This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanced Strategic Diagram (ESD), the study applies the PAGER framework to link Patterns, Advances, Gaps, Research directions, and Evidence-based policy implications. Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures, showing a convergence toward integrated ecosystem architectures where intelligence, trust, and infrastructure reinforce each other. Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration. Emerging themes such as explainable AI, federated learning, and privacy mechanisms highlight areas needing further research. Policy-relevant recommendations focus on coordinated governance, scalable infrastructure, and secure data ecosystems, particularly for developing country contexts. The study bridges bibliometric evidence with actionable policies, supporting informed decision-making in smart hospital development.

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arXiv2025

Bibliometric-enhanced Systematic Literature Review of EEG in Education: Learning Concepts, Computational Methods, and Research Opportunities

Application of electroencephalography (EEG) in educational research has grown substantially, yet a comprehensive integration of methodological frameworks, educational constructs, computational methods, and research gaps remains limited. This study applies a Bibliometric-enhanced Systematic Literature Review (BenSLR) to provide a systematic overview of EEG in education. Literature was extracted from Scopus, screened, and analyzed, with keyword co-occurrence evaluated using VOSviewer and emerging trends visualized through an Enhanced Strategic Diagram via BiblioPlot. Key findings include engagement, attention, and learning style as prominent constructs, with machine learning and deep learning frequently employed for modeling complex cognitive states. EEG signal processing, feature extraction, and assessment of cognitive and affective states were recurrent across studies. Innovative interventions such as virtual reality and neurofeedback demonstrate EEG's role in supporting adaptive and individualized learning experiences. Challenges remain in linking neural markers with observable learning behaviors, extending measurements beyond attention and working memory, and enhancing predictive model generalizability. The study demonstrates BenSLR's potential to integrate qualitative and quantitative perspectives and offers a transferable approach for other research areas to develop methodologies and evidence-based educational interventions.

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