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Methods in Science and Technology Studies
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31234232     DOI : https://doi.org/10.64539/msts
Core Subject : Engineering,
Engineering
The Methods in Science and Technology Studies (MSTS) (e-ISSN: 3123-4232) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal publishes research that focuses on methods, models, analytical approaches, and systematic studies in science, technology, and science- and technology-based education. It aims to support the development and application of scientific and technological methods in addressing research problems and practical challenges. The journal accepts original research articles and review papers that present methodological frameworks, experimental and analytical methods, computational models, and applied studies in science, technology, and education, including interdisciplinary and applied perspectives. Scope includes: Natural and applied sciences Engineering and technology studies Computational, mathematical, and data-driven methods Machine learning, artificial intelligence, and information technology Decision-making, optimization, and forecasting methods Science and technology–based education studies Legal and regulatory studies related to science and technology The journal provides a focused platform for methodological and applied studies in science, technology, education, and related regulatory contexts.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "Vol. 2 No. 1 (2026): June Article in Process" : 9 Documents clear
How to Utilize Generative AI to Compose Music through the Diary for the Possibility about User Experience and Service? Nahyun Woo; Jeahyun Choi; Hongmi Yang; Minju Kim; Jeongbin Choi; Soonkyu Jang
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.403

Abstract

This study explores the potential of generative AI to transform diary writing into a music-based creative activity, addressing the growing significance of AI-generated content in everyday cultural practices. While prior research has examined diary-based music composition and AI music interfaces, few studies have empirically evaluated the integration of generative AI music with diary content in terms of emotional response, service perception, and usability. To bridge this gap, this study aimed to design a conceptual AI-based diary app that converts personal diary entries into lyrics and automatically generates genre-specific music reflecting contextual data (e.g., date, weather, location, and emotion). An experimental evaluation was conducted with 73 participants who listened to four AI-generated songs composed from one month of diary entries and reviewed a prototype UI. Using a 5-point Likert scale, all key factors received positive evaluations above 4.0 (emotional enthusiasm: 4.39; emotional immersion: 4.31; sympathy: 4.21; convenience: 4.23; service immersion: 4.34; retention intention: 4.19). Multiple regression analysis revealed significant positive relationships between music immersion and service awareness (p < .01), with adjusted R² values ranging from 0.241 to 0.354. The System Usability Scale score averaged 86.13, exceeding the acceptability benchmark of 75. These findings indicate that diary-based AI music services can enhance emotional immersion and user retention through convenience and personalization. The study implies strong practical potential for generative AI-driven creative services and contributes empirical evidence for future AI-based cultural content design.
An Application of Manifold-Constrained Hyper-Connection in A Progressive Web App for Sovereign Debt Sustainability Analysis Don Charles
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.414

Abstract

Importance: The rise in Trinidad and Tobago’s (T&T’s) public debt from 61.9% of GDP in 2019 to 75.6% by 2024 highlights a fiscal vulnerability facing the country. Like many other Caribbean small open economies, it is highly susceptible to external shocks, that can rapidly escalate debt-to-GDP ratios, threatening long-term economic stability. Research Gap: While the IMF provides debt sustainability frameworks, their implementation requires advances mathematical skills and extensive data often unavailable in developing countries. Existing literature identifies vulnerability under current policies but offers limited actionable guidance on the specific fiscal adjustment required to achieve sustainability targets, creating an operational gap between diagnosis and practical planning. Objective: This study proposes and designs a computational framework for a PWA to derive the fiscal surplus required to bring the debt-to-GDP ratio to a sustainable level of 60% of GDP in 10 years. Methodology: The methodology forecasts 10-year GDP using Manifold-Constrained Hyper-Connections (mHC), computes target debt at 60% of GDP, calculates the difference from current debt, and amortizes this excess debt to determine required annual fiscal surplus. Key findings: Achieving the 60% target by 2034 requires reducing debt by US$2,072.57 million, necessitating a steady annual fiscal surplus of US$268.41 million at a 5% discount rate. Implications: This study contributes the first empirical mHC application for macroeconomic forecasting, a pragmatic debt sustainability framework operationalized with minimal data, and an accessible tool that integrates technical fiscal analysis for resource-constrained policymakers in developing economies.
Thermal Stability of EVA Nanocomposites for Solar Cell Encapsulation Ganiyu Olamide Ogunsiji; Oluwaseyi Omotayo Alabi; Adeoti Oyegbori Laoye; Saidat Abisoye Salisu; Samuel Adekunle Dada
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.385

Abstract

The long-term reliability and performance of photovoltaic (PV) modules largely depend on the thermal stability and durability of encapsulation materials that protect solar cells from environmental and thermal degradation. Ethylene–vinyl acetate (EVA) is widely used as a solar cell encapsulant due to its excellent optical and mechanical properties; however, its thermal stability and resistance to degradation remain critical challenges under prolonged operating conditions. Although EVA-based nanocomposites have been investigated for solar cell encapsulation, limited studies have systematically examined how different nanoclay fillers and processing conditions influence the thermal stability and encapsulation efficiency of EVA materials. This study aims to optimize the thermal stability of EVA nanocomposites by incorporating different inorganic fillers mica, montmorillonite (MMT), and vermiculite, at varying concentrations and milling cycles. An 8% EVA solution was prepared and blended with these fillers to evaluate their effects on the thermal and structural properties of the nanocomposite materials. Thermal characterization using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) revealed noticeable changes in melting temperature, glass transition temperature, and thermal degradation behavior. The incorporation of nanofillers improved the thermal stability of the EVA matrix and influenced its crystallinity and mechanical properties. The optimized EVA nanocomposite demonstrated enhanced thermal resistance and improved durability compared with neat EVA, although a slight reduction in encapsulation efficiency was observed. These findings provide valuable insights into the formulation and optimization of EVA nanocomposites for solar cell encapsulation, contributing to the development of more thermally stable and durable encapsulation materials for sustainable photovoltaic applications.
The Impact of the Modern Trends on the Complexing of the Business Model of High Tech IT Company Denis S. Pashchenko
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.402

Abstract

This article explores the critical transformation of IT firms into high-tech companies (HTC) amid rapidly evolving industry trends, including sector-wide digitalization, the shift toward full virtualization of production processes, and the increasing integration of artificial intelligence (AI) tools in software engineering. These trends are fundamentally reshaping the competitive landscape, necessitating a proactive response from IT companies seeking to maintain and enhance their market positions. The article introduces an innovative model for high-tech companies, placing the production function at the core of a company’s competitive strategy. This model emphasizes the need for companies to adopt digitalized, automated processes across all phases of the software lifecycle, fully embrace remote and hybrid work models, and develop unique products driven by digital management systems. Additionally, the article presents a comprehensive set of strategic elements and tactical steps that IT companies can implement to achieve HTC status. These include fully virtualized production functions, geographically distributed teams that enable continuous product support and development, and the deployment of AI tools to enhance operational decision-making, sales forecasting, and customer support. A key focus is on maximizing value creation from intellectual property rather than traditional revenue streams such as outsourced software development or service provision. The study also highlights the need for companies to integrate predictive models and AI tools in various business areas, including marketing, sales, and product management, to strengthen their market position.
Interpretable Academic Outcome Prediction Using Explainable Boosting Machines Godfrey Perfectson Oise; Felix Oshiorenoya Uloko; Kevin Chinedu Pius; Enovwo Eferoba–Idio; Michael Uyiosa Edobor; Evans Mintah; Osahon Ukpebor; Oludare Sokoya; Tejiri Jessa
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.441

Abstract

Predictive analytics has become an important component of learning analytics in higher education, enabling institutions to identify academic risks and support student success through data-driven decision making. However, many existing academic outcome prediction models rely on complex black-box machine learning techniques that provide high predictive performance but limited transparency and interpretability. The lack of explainability restricts the practical adoption of such models in educational environments where accountability, trust, and ethical decision-making are essential. This study proposes an interpretable machine learning framework for multi-class academic outcome prediction using the Explainable Boosting Machine (EBM), a glass-box model that combines the predictive power of ensemble boosting with the transparency of generalized additive models. The proposed framework was evaluated using a publicly available Student Performance and Learning Behavior dataset consisting of 6,519 student records containing academic, behavioral, and demographic attributes. Academic outcomes were formulated as a four-class classification task: Distinction, Pass, Fail, and Withdrawn. Model performance was assessed using multiple evaluation metrics including accuracy, precision, recall, F1-score, and ROC–AUC analysis. Experimental results demonstrate that the proposed EBM model achieves an accuracy of 88% and an overall ROC–AUC score of 0.91, indicating strong predictive capability across outcome categories. Furthermore, the model provides native interpretability through feature contribution functions and SHAP-based explanations, enabling both global and instance-level understanding of predictions. The results demonstrate that reliable academic outcome prediction can be achieved without sacrificing interpretability, providing a transparent and trustworthy decision-support framework for educational analytics.
A Hybrid Machine Learning–Optimization Framework for Energy Demand Forecasting and Decision Support in Smart Infrastructure Godfrey Perfectson Oise; Tejiri Jessa; Evans Mintah; Felix Oshiorenoya Uloko; Oludare Sokoya; Osahon Ukpebor
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.440

Abstract

This study addresses the growing need for accurate and actionable energy demand forecasting in smart infrastructure systems, where data-driven decision-making is essential for efficiency, sustainability, and system reliability. Despite advances in machine learning-based forecasting, most approaches remain prediction-centric and are rarely integrated with operational optimization and decision-support mechanisms, limiting their real-world applicability. To address this gap, this study proposes a sequentially integrated hybrid machine learning–optimization framework that combines ensemble-based forecasting, optimization-driven energy allocation, and explainable artificial intelligence (XAI) within a unified architecture. The term hybrid denotes the integration of heterogeneous methodological components, while the framework is implemented as a pipeline in which forecasting outputs inform downstream optimization. The predictive module incorporates XGBoost and Long Short-Term Memory (LSTM) models, alongside an ensemble approach that operates within the forecasting stage to enhance robustness and generalization. The optimization component utilizes forecasted demand to minimize energy cost under demand and capacity constraints, while SHAP-based analysis improves interpretability and transparency. Empirical evaluation using the UCI Building Energy Efficiency dataset shows that XGBoost achieves the highest predictive accuracy (MAE = 0.429, RMSE = 0.613, R² = 0.996), while the ensemble model provides strong robustness (R² = 0.994). The integrated framework effectively smooths demand fluctuations, improves allocation efficiency, and identifies relative compactness and glazing area as dominant features. The results demonstrate that sequential integration of forecasting, optimization, and interpretability enhances predictive reliability, operational efficiency, and decision transparency.
Impact of Generative AI on Student Learning in Higher Education using Robust Assessment Metrics Framework Adesola M. Falade; Ayobami E. Mesioye
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.415

Abstract

The rapid emergence of Generative Artificial Intelligence (GAI) has transformed the landscape of higher education, influencing pedagogy, assessment, and student learning experiences. Despite its widespread adoption, a significant research gap persists regarding the empirical measurement of its impact on specific learning outcomes. While GAI tools are widely adopted, existing assessment frameworks often fail to distinguish between machine-generated efficiency and genuine cognitive development. This study addresses this gap by developing the Robust Assessment Metrics Framework (RAMF), evaluated through a mixed-methods approach involving students and faculty (N=295) at McPherson University. Quantitative findings reveal a significant "Efficiency-Cognition Trade-off": while frequent GAI usage significantly enhances task efficiency (p < 0.001), it correlates with a statistically significant decline in critical thinking (p < 0.01) and self-reported originality (p < 0.01). Interestingly, regression analysis shows that AI literacy and institutional policy clarity are stronger predictors of academic confidence than usage frequency. This suggests a psychological "confidence paradox" where students feel more capable despite lower cognitive engagement. Qualitatively, thematic analysis highlights a shift toward "shortcut learning" that necessitates a move from product-oriented to process-oriented evaluation. The RAMF introduces expert-validated protocols such as the ‘30/70 Synthesis Rule’ and "Process Logs," to safeguard academic rigor. This research provides institutional leaders with an expert-validated framework proposed for institutional trial to shift from product-oriented to process-oriented assessment in the AI era. By focusing on the interplay between human agency and algorithmic assistance, this research offers broader implications for pedagogical redesign in an AI-saturated academic environment.
Energy-Efficient Federated Learning with Temporal Convolutional Networks for Intrusion Detection Oise, Godfrey Perfectson; Uloko, Felix Oshiorenoya; Pius, Kevin Chinedu; Oshasha, Roli Lydia; Osemwegie, Eric Edeigue; Obrorindo, Immunhierokene Clinton
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.462

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface of modern network infrastructures, necessitating intelligent and scalable intrusion detection systems. Federated Learning (FL) has emerged as a promising paradigm for distributed model training without centralized data sharing; however, challenges such as energy efficiency, data heterogeneity, and privacy preservation remain inadequately addressed. Existing studies often emphasize optimization objectives theoretically without validating them under realistic constraints. This paper proposes an energy-aware federated learning framework integrating Temporal Convolutional Networks (TCNs) for intrusion detection using distributed network traffic data. The framework incorporates differential privacy for secure model updates and a conceptual energy-aware client participation strategy. Experiments are conducted on the UNSW-NB15 dataset under a controlled setting with fixed client participation and communication parameters. The results demonstrate that the proposed model achieves improved classification accuracy and stable convergence behavior across communication rounds while operating under a fixed energy budget. However, energy consumption remains constant due to controlled experimental conditions, indicating that the study evaluates performance under energy constraints rather than dynamic energy optimization. The findings highlight the effectiveness of TCN-based federated models for intrusion detection in resource-constrained environments. Future work will focus on dynamic energy modeling, heterogeneous client environments, and comprehensive multi-objective evaluation.
Defending AI Sentinels: A Multi-Layered Runtime Security Architecture for Generative AI in AIOps Mesioye, Ayobami E.; Adeduro, Oladapo O.; Oluwagbemi, Johnson B.
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.420

Abstract

The rapid integration of Generative AI into Automated IT Operations (AIOps) has introduced "AI Sentinels", an autonomous agents capable of managing critical infrastructure. However, these systems introduce a novel attack surface evidenced in inference-time adversarial manipulations such as prompt injection and jailbreaking. While existing security paradigms protect network perimeters, they fail to safeguard the internal logic of AI agents, creating a research gap in runtime defense for autonomous infrastructure controllers. This study aims to develop a multi-layered, defense-in-depth architecture to neutralize these threats. The proposed system integrates three layers: an Intent Validation Engine (Layer 1) using semantic analysis, a Secure Sandbox (Layer 2) utilizing eBPF-based kernel monitoring within a digital twin, and a Static Analysis module (Layer 3) for infrastructure-as-code (IaC) compliance. Key findings indicate that while single-layer defenses achieve an Adversarial Success Rate (ASR) of 32–68%, the proposed multi-layered approach reduces the ASR to near-zero (0.2% in robust testing), maintaining an F1-score of 0.990. Despite the complexity of the pipeline, the system achieves a mean operational latency of 48.2ms on enterprise-grade hardware (NVIDIA A100). These implications suggest that runtime behavioral verification is essential for the safe deployment of LLMs in privileged environments, providing a foundational framework for resilient AIOps.

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