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Widi Aribowo
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Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 30640768     DOI : https://doi.org/10.26740/vubeta.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Engineering Mechanical Engineering Transportation
Vokasi Unesa Bulletin Of Engineering, Technology and Applied Science is a peer-reviewed, Quarterly International Journal, that publishes high-quality theoretical and experimental papers of permanent interest, that have not previously been published in a journal, in the field of engineering, technology, and applied sciences that aim to promote the theory and practice of Engineering, Technology And Applied Science.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 118 Documents
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A Unified Framework for Hyperbolic and Ambiguous Spaces Anoop Kumar Tiwari
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.47699

Abstract

The concept of space has been a fundamental aspect of mathematical and computational modeling, with Euclidean and hyperbolic spaces serving as classical frameworks. This article explores the transition from hyperbolic space to ambiguous space, establishing a novel comparative framework that integrates gyrovector spaces and ambiguous set theory. Hyperbolic space, characterized by non-Euclidean geometry, forms the foundation for many applications, including machine learning and network analysis. Gyrovector space, an extension of vector space under Möbius addition, provides a computationally efficient model for hyperbolic geometry. In contrast, ambiguous sets introduce a four-dimensional membership structure, enabling more nuanced representations of uncertainty and vagueness in decision-making contexts. The concept of ambiguous space is then developed as a generalized mathematical structure that incorporates elements from both hyperbolic geometry and ambiguous set theory. Finally, we demonstrate the applicability of ambiguous space in customer segmentation, where traditional clustering methods often fail to capture complex consumer behavior.
Comparative Analysis of Embedded Systems for Mobile Health Applications Against Cyber Attacks: A Review Study Anayo Chukwu Ikegwu; Excellence Essien Akparawa; Uzoma Rita Alo
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.44150

Abstract

Mobile health applications and embedded systems have transformed healthcare by offering real-time monitoring, remote diagnostics, and better patient outcomes. However, reliance on digital health solutions introduces significant cybersecurity challenges, requiring robust security measures to protect sensitive patient data. This paper discusses the security needs of embedded systems in mHealth, emphasizing the importance of confidentiality, integrity, and availability to safeguard data. It reviews compliance frameworks such as HIPAA and GDPR, which set data protection standards. The research highlights the need for cybersecurity to support patient safety, mitigate risks after device compromise, and defend against emerging threats. Comparative studies of current security technologies, both hardware- and software-based, show their impact against cyberattacks. Finally, the paper discusses trends such as AI-driven threat detection, post-quantum cryptography, and edge computing as future mHealth security paradigms. By adopting strong security protocols, healthcare institutions can boost trust, meet regulations, and secure mHealth embedded systems.
A Concise Comparative Analysis of Ambiguous Set Theory in Relation to Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets Pawan Kumar Singh
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.46080

Abstract

Uncertainty, vagueness, indeterminacy, and ambiguity are fundamental challenges in representing information arising from complex real-world environments, particularly when human perception and decision-making involve indistinct boundaries between truth and falsity. This article presents a conceptual review and comparative analysis of Ambiguous Set (AS) theory in relation to Fuzzy Set (FS), Intuitionistic Fuzzy Set (IFS), and Neutrosophic Set (NS). The objective is to clarify the structural and representational characteristics of AS and identify situations in which its formulation may provide additional flexibility for uncertainty modeling. The comparison considers the basic components, mathematical constraints, relationships among parameters, interpretation of uncertainty, and capability to represent human perception. FS represents uncertainty through a single membership degree, whereas IFS incorporates membership, non-membership, and hesitation. NS further introduces independently characterized truth, indeterminacy, and falsity components. In contrast, AS employs four interrelated components: true membership, false membership, true-ambiguous membership, and false-ambiguous membership, allowing partially true and partially false conditions to be explicitly represented. The reviewed literature indicates that AS has been explored in image processing, multi-criteria decision-making, ambiguous logic, mathematical modeling, complex analysis, and time-series applications. However, its additional parameters may increase computational and interpretational complexity. Therefore, AS should not be regarded as universally superior to FS, IFS, or NS; its suitability depends on the characteristics of the uncertainty and application context. This review provides a balanced assessment of the strengths, limitations, and potential applications of AS while identifying future research opportunities in machine learning, hybrid decision-making, temporal ambiguity modeling, cognitive artificial intelligence, optimization, and formal mathematical development.
Privacy-Enhanced Federated Learning Model for Secure Internet of Things Applications: A Systematic Review Mohammed Ajuji; Yusuf Musa Malgwi; Asabe Sandra Ahmadu
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.46474

Abstract

The rapid growth of Internet of Things (IoT) ecosystems has increased security and privacy risks due to the large volume of sensitive and distributed data generated by connected devices. Traditional machine learning approaches for IoT security rely on centralized data aggregation, which raises privacy concerns, scalability limitations, and regulatory challenges. This study presents a systematic literature review of privacy-enhanced federated learning (PEFL) for secure IoT applications. The review analyzes federated learning architectures, optimization strategies, privacy-preserving mechanisms, and cybersecurity use cases in heterogeneous IoT environments. Findings indicate that federated learning improves privacy by keeping data localized at edge devices, while techniques such as differential privacy and secure aggregation enhance confidentiality. However, these methods introduce trade-offs in accuracy, communication efficiency, and computational overhead. Hierarchical and adaptive FL architectures improve scalability and robustness, particularly for intrusion detection systems. Persistent challenges include non-IID data, device heterogeneity, limited edge resources, and the lack of standardized benchmarks and real-world deployments. PEFL offers a promising framework for securing IoT systems, though further research is needed to address deployment and performance constraints in practical environments.
Data-Driven Modeling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning Ajayi Ore-Ofe Ajayi Ore-Ofe; Muhammad Bashir Yahya; Shehu Mohammed Yusuf; Abubakar Umar; Hassan Maharazu; Ibrahim Ibrahim; Murtala Abduljalil Ahmad
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.47643

Abstract

Internal displacement in Nigeria remains a major humanitarian challenge driven by conflict, insurgency, communal clashes, and climate-related disasters. Existing responses are often reactive, highlighting the need for data-driven approaches to support proactive humanitarian planning. This study, titled Data-Driven Modeling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning, develops a machine learning framework to analyze and predict displacement trends using data from the International Organization for Migration (IOM) Displacement Tracking Matrix, particularly the Baseline Assessment and Needs Monitoring surveys. The dataset comprised over 114,000 records and 19 variables, including demographic characteristics, site accessibility, household size, and priority needs. Data preprocessing involved missing-value handling, categorical standardization, one-hot encoding, and numerical normalization. The highly imbalanced distribution of displacement causes was also considered, with insurgency accounting for 95% of cases, communal clashes 4%, and natural disasters 1%. Exploratory analysis identified Borno, Adamawa, and Yobe as the most affected states, with insurgency consistently dominating and September 2022 recording the highest displacement peak. Logistic Regression, Random Forest, and XGBoost were evaluated. XGBoost achieved the best performance, obtaining a macro F1-score of 0.96 and balanced accuracy of 0.968. It demonstrated strong classification performance for communal clashes (F1 = 0.99) and natural disasters (F1 = 0.90), while correctly identifying nearly all insurgency cases. These findings demonstrate that displacement patterns can be effectively modeled using socio-economic and geographic indicators. The proposed framework can assist humanitarian agencies in anticipating displacement, optimizing resource allocation, and strengthening early-warning systems to improve crisis preparedness and resilience.
Topology Optimization of Passive UHF RFID Systems for Constrained Environments Rahma Zayoud; Habib Hamam
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.49241

Abstract

Radio Frequency Identification (RFID), a remote identification and localization technology, utilizes electromagnetic and magnetic field properties and signal processing. However, this technology suffers from sensitivity issues such as liquids and metals, object movement, and collisions, which negatively affect its reading rate. To address this complex problem, we first demonstrate, based on theoretical principles, the strong relationship between the physical RFID topology configuration and its reading rate. Subsequently, we developed an application that determines the best possible reading rate in a hostile environment—where all RFID constraints are present—by optimizing the physical topology configuration. For this purpose, we used the simulated annealing algorithm as an optimization technique. The results are promising: the algorithm converged three times to the same optimal solution within a single run; it required 48.5% of total iterations to find this configuration, which achieved a reading rate of 42.5% in a hostile environment. This optimal rate is considered satisfactory, especially when compared to the initial configuration's reading rate of approximately 7.5% under the same conditions. Even across multiple runs in different environments, the algorithm shows good consistency
The ShuBAM-TFNet: A Lightweight Temporal-Frequency Attention Network for Edge-Oriented Environmental Sound Recognition Kabiru Muhammed Nasiru; Shehu Mohammed Yusuf; Sani Saleh Saminu
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.50353

Abstract

Efficient environmental sound recognition is essential for smart-city monitoring and edge-based acoustic sensing, yet practical deployment is often constrained by the computational cost and parameter size of deep learning models. This paper presents ShuBAM-TFNet, a lightweight convolutional neural network designed to achieve high recognition accuracy while remaining suitable for resource-limited devices. The proposed architecture processes Log-Mel spectrograms using dilated and time-distributed convolutional layers to capture long-range temporal dependencies and localized temporal patterns without increasing model complexity. A lightweight temporal–frequency attention mechanism is introduced to adaptively emphasize informative regions across time and frequency dimensions, while optional Bottleneck Attention Modules are examined for channel and spatial refinement. The model is evaluated on UrbanSound8K and ESC-10 using standard protocols. On UrbanSound8K, the best-performing variant (without Bottleneck Attention Modules) achieves 91.76 ± 0.90% accuracy with only 70,754 trainable parameters, converging in approximately 26 epochs. Ablation studies confirm that temporal–frequency attention provides the most significant performance gain (≈2–3%), while dilated convolutions accelerate convergence without increasing parameters. Cross-dataset evaluation on ESC-10 achieves 84.00 ± 2.41% accuracy and a macro F1-score of 0.838, demonstrating reasonable generalization. The counterintuitive finding that removing Bottleneck Attention Modules improves performance reveals that channel-spatial attention requires task-specific validation. These results indicate that attention-guided lightweight architectures particularly those emphasizing temporal-frequency modeling over channel-spatial refinement provide a practical alternative for environmental sound recognition on resource-constrained edge devices.
Consensus Optimization for Energy Efficiency in Heterogeneous Wireless Sensor Networks: Models, Techniques, and Future Directions Matthew Iyobhebhe; Abdoulie Momodou. S Tekanyi; Elvis Obi; Shamsudeen. A. Mikail; Njoku Franklin. C; Botson Ishaya Chollom; Paul Thomas Muge; Athanisius Terlumun Utev; Ridwan. O Eleshin; Fatima Ashafa; Asebakeoghene J. Khama
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.50510

Abstract

Energy efficiency continues to be a major concern in heterogeneous wireless sensor networks (HWSNs) due to unequal resource allocation and varying network dynamics. Consensus optimization offers an effective paradigm for enabling distributed management and achieving balanced energy utilization among heterogeneous sensor nodes. This review presents an in-depth examination of consensus-based models and techniques aimed at improving energy efficiency in HWSNs. It categorizes existing methodologies based on their operational mechanisms and analyzes how node heterogeneity impacts consensus formulation and overall network performance. In addition, it highlights key research limitations, including the lack of standardized classifications, inconsistent energy modeling, insufficient benchmarking, and limited analysis of the tradeoff between convergence and energy overhead. By synthesizing advances in adaptive weighting, clustering mechanisms, and hierarchical consensus architectures, the study proposes future research pathways for scalable, robust, and energy-efficient consensus systems. Ultimately, this review provides a foundation for developing sustainable optimization frameworks for next-generation sensor networks.
A Systematic Review of Artificial Intelligence in Transportation Systems: Methods, Applications, and Transport–Energy Integration for Sustainable Mobility Najeem Olawale Adelakun; Matthew Babatunde Olajide; Samuel Adeniyi Omolola
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.52572

Abstract

Artificial Intelligence (AI) is transforming the transportation systems with autonomous, efficient, and sustainable mobility. The swift transformation of it into a data-driven intelligence rather than a rule-based one has opened new possibilities of interconnecting transportation systems with electrical power networks. This study is a systematic review of AI applications in autonomous and sustainable transportation systems. The major academic databases, such as Scopus, IEEE Xplore, Web of Science, ScienceDirect, and SpringerLink, were searched to retrieve literature that was published in 2015-2025. Peer-reviewed journal and conference papers were screened using a structured screening process, which led to the selection of 35 studies to be included in qualitative synthesis. The review framework was based on thematic analysis in the fields of evolution, applications, methodologies, and energy integration. Results indicate that machine learning and deep learning are the most prevalent in the current transportation AI studies, especially in traffic prediction, autonomous navigation, and infrastructure monitoring. The most developed application areas are traffic management and autonomous vehicle systems, and energy-aware mobility, especially electric vehicle charging optimisation and vehicle-to-grid coordination, is developing quickly but is less advanced. Major obstacles are the data quality constraints, the challenges of model interpretability, the constraints of infrastructure, cybersecurity threats, and disjointed regulatory frameworks, particularly in developing areas. The review confirms the evident shift towards combined transportation-energy intelligence, where AI can facilitate coordinated mobility and power system optimisation. The future directions are federated learning, digital twin systems, explainable AI, and multi-modal intelligent transport systems, which facilitate scalable and sustainable mobility development.
Numerical Method for Solving Unsteady Squeezing Magnetohydrodynamic Eyring-Powell Fluid Model with Variable Thermophysical Properties adeyemi BEPO; Oderinu Rasaq Adekola; Akindele Akintayo Oladimeji; Ogunniyi Oluyoola Dorcas; Oladapo Olayinka Akeem
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.52684

Abstract

This investigation studies an unsteady squeezing magnetohydrodynamic flow of an Eyring–Powell fluid confined between two parallel plates with variable thermophysical properties, with an Arrhenius–type chemical reaction occurring simultaneously. The coupled nonlinear equations- the momentum one, then energy, and concentration too- are reshaped into dimensionless ordinary differential equations using similarity transformations, which kind of simplifies things, at least in the mathematical sense. After that, they craft a unified semi-numerical approach involving Chebyshev polynomials, a Galerkin weighted-residual framework, and Gauss–Legendre quadrature, so the numerical results can be obtained accurately without too much complication. They also provide a convergence analysis, which shows that stable solutions are reached at low polynomial order (N = 12), which is a good sign. Later, the authors cross-check the strategy against benchmark results reported by Ghadikolaei et al. (2017), and the matching outcomes support the methodis accuracy. In the results section, increasing electrical conductivity strengthens the Lorentz force and suppresses the velocity field, while higher thermal conductivity enhances the temperature distribution. Also, variable mass diffusivity thickens the concentration boundary layer, and if activation energy increases, the reaction rate slows, which then increases species concentration. Finally, they compute the skin friction coefficient, the Nusselt number, and the Sherwood number, which are used to quantify momentum, heat, and mass transfer, respectively. Overall, the Chebyshev–Galerkin scheme shows rapid convergence, reliable numerical stability, and good accuracy for nonlinear MHD non-Newtonian flow problems that include variable properties and reactive transport.

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