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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 26 Documents
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Comparative Analysis of Machine Learning Algorithms for Predictive Maintenance Odugbesan Olusegun Abayomi; Akinola Emmanuel Kayode; Oyedele Oluwasanya; Ayobami Emmanuel Mesioye
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.505

Abstract

The transition toward Industry 4.0 has established predictive maintenance (PdM) as a critical strategy for optimizing operational efficiency and reducing unexpected downtime through real-time sensor data analytics. However, the practical implementation of PdM is frequently hindered by the extreme class imbalance inherent in industrial datasets, where equipment failure events are significantly rarer than normal operating hours. This study presents a comprehensive comparative evaluation of five machine learning algorithms—Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR)—utilizing the AI4I 2020 Predictive Maintenance Dataset. By implementing a rigorous preprocessing pipeline that employs Z-score normalization for feature scaling and the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate majority-class bias within the training partition. The models were evaluated on a hold-out test set of 2,000 instances using metrics including accuracy, precision, recall, and the F1-score. Results indicate a pronounced “Accuracy Paradox” in linear and distance-based models; while KNN and Logistic Regression achieved deceptively high accuracies exceeding 96%, they failed to reliably detect actual failure signatures. In contrast, Random Forest emerged as the superior architecture, achieving an F1-score of 97.10% and a recall of 98.53%, correctly identifying 67 out of 68 failure instances. This research concludes that F1-score and Recall are more vital indicators of industrial reliability than simple accuracy. The findings provide a standardized framework for selecting ensemble-based classifiers to support scalable, data-driven maintenance strategies in modern smart manufacturing environments.
MLHP: A Multi-Level Handoff Prioritization Framework for Service Differentiation in Buffered Systems Oluwasanya Oyedele; Akinniyi Stellamaris Omowunmi; Akinola Kayode Emmanuel
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.513

Abstract

In modern wireless networks, efficient handoff strategies are crucial for several services with various Quality of Service (QoS) requirements. However, a significant research gap exists as most current handoff techniques treat all internet traffic uniformly, leading to performance degradation, latency, and glitches for time-sensitive applications like Ultra-Reliable Low Latency Communication (URLLC) during network transitions. To address this, the main objective of this study was to develop the Multi-Level Handoff Prioritization (MLHP) framework specifically for buffered handoff setups. The MLHP system integrates three core components: a multi-level service classifier, a dedicated buffering architecture with dynamic thresholds, and a hybrid scheduling mechanism combining Strict Priority and Weighted Fair Queuing. Simulation results reveal that MLHP significantly outperforms both traditional Non-Prioritized Buffered Handoff (NPBH) and Dynamic Queue Management (DQM) schemes. Key findings indicate that MLHP maintains a low dropping probability of approximately 6% under high handoff frequencies and achieves an aggregate throughput exceeding 44 Mbps during high mobility scenarios, while successfully maintaining sub-10 ms delays specifically for mission-critical URLLC traffic. The broader implications of this study suggest that MLHP provides a scalable and flexible solution for handoff management, effectively meeting the stringent requirements of 5G-and-beyond networks. By ensuring granular service differentiation, the framework enhances overall network reliability and user experience in increasingly heterogeneous mobile environments.
Trust–Privacy-Based User Acceptance Model for Location-Based Mobile Navigation Services: Conceptual Development and Research Propositions Atta Ur Rahman; Fuyang Ke; Muhammad Raza
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.522

Abstract

The two most important factors for location-based mobile navigation services are navigation performance and trusted data governance, especially when location data is used repeatedly. Previous studies have tended to focus on privacy concerns, perceived risk, trust, transparency, control, and service quality separately or in settings other than consumer navigation, as is the case in this study. As a result, the interaction between privacy risk perceptions and provider assurances in influencing mobile navigation acceptance remains poorly defined. This conceptual study develops a trust- and privacy-based user acceptance framework by synthesizing the literature through a structured procedure and incorporating the Technology Acceptance Model, Privacy Calculus Theory, and Trust Theory. The framework posits that privacy concern increases perceived risk, while privacy concern and perceived risk reduce trust. In contrast, transparency, perceived control over location data, and service reliability can enhance trust. The roles of perceived usefulness and user acceptance intention in the pathway to user acceptance remain unchanged, suggesting that trust is an antecedent to perceived usefulness and user acceptance intention. The framework fills the theoretical gap between privacy-risk assessment and trust-building service attributes, and between trust in technology acceptance and repeated disclosure of the location setting. It also recognizes the need for transparent data practices, meaningful location control, and reliable service performance as provisional design priorities, which require future empirical validation. The propositions are to be used as a starting point for further testing on platforms, providers, user groups, cultures, and regulatory frameworks.
Compliance-Aware AI Framework for Anti-Interference and Adaptive Routing in Telecommunication Networks Godfrey Wandwi; Dennis Numi Madaha
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.551

Abstract

The rapid evolution of heterogeneous telecommunication networks has increased the difficulty of maintaining communication performance while operating within spectrum and transmission constraints. Existing network optimization approaches primarily focus on throughput, routing, and interference mitigation, whereas regulatory compliance is commonly treated as a separate post-processing activity. This separation limits the ability of network-control mechanisms to respond proactively to compliance risks. This study develops a Compliance-Aware AI Framework (CA-AIF) that integrates Coalition Formation Games (CFG), variance-aware DUCBQ adaptive routing, and Proximal Policy Optimization (PPO) with a policy-constrained compliance monitoring layer. The framework represents compliance risk through telemetry-derived violation indicators associated with spectrum occupancy, transmission power, and interference conditions and incorporates these indicators into network decision-making. A controlled simulation environment is developed using synthetically generated mobility, interference, channel, and spectrum-occupancy data. The proposed framework is evaluated against Q-learning, Dyna-Q, UCBQ, and DUCBQ using throughput, packet acceptance, routing stability, convergence, compliance violation rate, and computational cost. Across repeated simulations, the proposed framework achieves higher throughput and packet acceptance while reducing compliance violations relative to the evaluated baselines. The results demonstrate that incorporating compliance risk into network optimization can improve the stability of communication decisions without treating compliance as an independent post-processing task. The study contributes a reproducible simulation-based framework for compliance-aware intelligent networking and provides a basis for future validation using operational spectrum-monitoring and regulatory datasets.
Enterprise Integration Architecture using Event Mesh: A Reference Framework for Cloud-Native and AI-Ready Enterprises Padmanabham Venkiteela
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.559

Abstract

Modern enterprises integrate heterogeneous on-premises, multi-cloud, SaaS, IoT, and AI-enabled systems, creating coordination, scalability, resilience, governance, and real-time context challenges for conventional integration approaches. Although Event-Driven Architecture and event streaming are established, the literature provides limited vendor-neutral guidance that integrates Event Mesh federation, event governance, observability, hybrid-cloud deployment, and selected agentic-AI integration concerns into one enterprise reference framework. This study proposes the Enterprise Event Mesh Reference Architecture (EEMRA) to address that gap. Using Design Science Research Methodology, the study identifies the integration problem, synthesizes prior literature, develops the EEMRA artifact, performs a qualitative capability comparison, and examines illustrative enterprise cases. The framework organizes enterprise systems, event enablement and production, the Event Mesh fabric, integration services, event consumers, and cross-cutting governance and observability. Principal contributions include EEMRA, a six-responsibility architecture model, the Enterprise Event Taxonomy Framework, an Agentic Integration Architecture, an Event Mesh + Model Context Protocol integration pattern, and the Enterprise Nervous System Principle. Findings indicate that Event Mesh can provide a useful asynchronous, federated integration substrate for event-reactive workloads, while performance and business-impact claims remain implementation-dependent and are not experimentally validated in this study. The broader implication is a structured, vendor-neutral basis for designing governed event fabrics and integrating them with broader AI-agent architectures.
A Machine-Learning Model for Identifying Non-Credible Scholarly Journals Fouad Abdulameer Salman; Bakhtawar Baluch; Zuriana Abu Bakar; Assane Lo
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.570

Abstract

The rapid growth of scholarly publishing has increased the need of reliably identify credible and non-credible journal websites. Evaluating these entities requires close, cautious, thorough, and at times skeptical scrutiny by researchers, institutions, and funding agencies. However, existing automated detection approaches appear to be customized for specific aspects of the criteria and do not effectively detect non-credible entities. Therefore, in this paper, we come up with a comprehensive dataset comprising 4,174 manually verified journal websites. We classify the dataset into two categories: credible journals and non-credible journals, including predatory, commercial, and hijacked journals. We also represent 36 new features derived from different resources of the journal websites. We used these new features to train and test the proposed model to improve accuracy. XGBoost achieved a precision of 0.9778, recall of 0.9601, F1-score of 0.9689, and accuracy of 97.86%, outperforming both GBC and Random Forest in the experimental results. Feature-group experiments show that Web-Based System and Academic Integrity features substantially improve overall classification performance, while HTML-based characteristics provide strong discriminatory power. The proposed approach shows the potential of integrating technical, structural, and scholarly-verification signals to support automated journal-credibility assessment and give researchers an efficient way to identify potentially non-credible scholarly venues.

Page 3 of 3 | Total Record : 26

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