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Widi Aribowo
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vubeta@unesa.ac.id
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Jl. Prof. Moch Yamin, Ketintang, Kec. Gayungan, Surabaya, Jawa Timur 60231
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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 20 Documents
 1   2 
Search results for , issue "Vol. 2 No. 2 (2025)" : 20 Documents clear
Design of a Class AB Power Amplifier For 5G Applications Joshua Aa-Daaryeb Nounyah; Bernice Ansu Pormaa; Emmanuel Mensah; Abdul-Rahman Ahmed; Raymond Gyaang
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.37801

Abstract

This work discusses the utilization of GaN HEMT technology on Rogers substrate in the design as well as the analysis of a 200 MHz Class AB power amplifier(PA)tailored for use in the 5G sub 6 GHz frequency band, specifically targeting 2.4 GHz applications.This is to satisfy the efficiency and linearityconstraints in typical 5G communications systems, especially at the input part of the communication chain,whiles realizing all round practical Figure of Merits (FoMs).Matching networks were devised employing cascaded L-section microstrip transmission lines, meticulously optimized foroptimum output power, return loss, and PAE. This demonstrates the effectiveness of the design approach in producing substantial power with heightened efficiency. Furthermore, the design exhibited enhanced linearity, even in the absence of commonly utilized feedback networks such as voltage dividers or emitter/source degenerationdue to the inherent robustness of the proposed design.The PA’s performance aligned exceptionally well with theoretical predictions. Electromagnetic simulation results showed a small signal gain of 13.634 dB with return losses maintaining below -12 dB across the desired operational bandwidth. Also, a power output of40.052 dBm for a 29 dBm input power was obtained, coupled with a PAE of 54.148%.
A Novel Hybrid Algorithm for Effective Image Restoration Zangana, Hewa; Firas, Mahmood Mustafa; Omar, Marwan
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.38118

Abstract

Image restoration plays a pivotal role in various applications, from medical imaging to satellite photography, by enhancing the quality of images degraded by noise, blur, or other distortions. Traditional methods and deep learning techniques have both shown promise in addressing these challenges, yet each has its limitations. Traditional algorithms often struggle with complex distortions, while deep learning models demand extensive computational resources and large datasets. To harness the strengths of both approaches, we propose a novel hybrid algorithm that integrates traditional image restoration techniques with advanced deep learning models. This paper presents a novel hybrid algorithm for image restoration, integrating traditional Wiener filtering with a state-of-the-art U-shaped transformer (Uformer) architecture. Unlike existing methods, our approach combines the computational efficiency of classical techniques with the robustness and precision of deep learning. Comprehensive evaluations on benchmark datasets demonstrate significant improvements in restoration quality (PSNR/SSIM) and computational efficiency compared to state-of-the-art methods. This research contributes a new perspective on hybrid methodologies, bridging the gap between traditional and modern approaches in image restoration.
The Use of Genetic Algorithm Optimization Approach in Comparison with Lambda Iteration Technique to Solve Economic Load Dispatch Problem Sabo Aliyu; Sadiq N. Buba; Olutosin Ogunleye; Kabir Mohammed; Samuel Ephraim Kalau; Daramdla P. Olaniyi
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.38275

Abstract

The increasing demand for efficient and reliable power generation systems has amplified the importance of solving Economic Load Dispatch (ELD) problems. This study compares the performance of two optimization techniques—Genetic Algorithm (GA), a robust metaheuristic approach, and Lambda Iteration, a traditional iterative method—on the IEEE 39-bus 10-generator test system. The analysis focuses on fuel cost minimization and computational efficiency. GA achieves a significant reduction in total fuel cost to $1390.29, outperforming Lambda Iteration's $2324.22. However, Lambda Iteration demonstrates faster convergence at 0.2 seconds compared to GA's 1.2 seconds. The results underscore the trade-offs between cost efficiency and computational speed, providing valuable insights into the suitability of advanced optimization methods like GA for complex ELD problems and the practicality of Lambda Iteration for simpler systems.
Microgrid Control Techniques: A Review Abdulmalik Ibrahim Dano; Sabo Aliyu; Olutosin Ogunleye ; Abdul Wahab Noor Izzri; Hossein Shahinzadeh; Abdulmajid Muhammad Na’inna
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36477

Abstract

Microgrids (MGs) are localized energy systems that integrate distributed energy resources (DERs) such as renewable energy, energy storage systems (ESS), and conventional generation sources. A critical challenge in the operation of microgrids is maintaining frequency stability, particularly during transient disturbances or load imbalances. This review provides a comprehensive analysis of various frequency control strategies employed in microgrids to ensure stable and reliable operation. The paper categorizes existing approaches into primary, secondary, and tertiary frequency control methods, evaluating their mechanisms, advantages, and limitations. Primary control focuses on immediate frequency regulation through local droop control, while secondary control ensures the restoration of frequency to its nominal value through centralized or decentralized coordination. Tertiary control manages economic dispatch and energy optimization for long-term stability. Additionally, the review addresses the impact of DER characteristics, such as variability and intermittency, on frequency regulation, and discusses advanced techniques, including model predictive control, fuzzy logic control, and Neural network control. The paper concludes with a discussion on future trends in microgrid frequency control, emphasizing the need for robust encryption and intrusion detection systems that protect microgrid control networks from cyber threats, ensuring reliable frequency regulation even in the event of a cyber-attack.
Real-Time Energy Demand Forecasting and Adaptive Demand Response Optimization for IoT-Enabled Smart Grids Aliyu Musa Kid; Ahmed, Muhammed Zaharadeen; Abdulkadir Hamidu Alkali; Jafaru Usman; Aisha Hassan Abdalla Hashim
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36818

Abstract

The evolution of energy systems concerning IoT-enabled smart grids require new innovative solutions to address enormous open issues in demand-supply balance, grid reliability, and sustainability. In this research work, attention is centered on integrating real-time energy demand forecast and adaptive demand response optimization. This is solely to improve efficiency and resilience of modern smart grids. We use Advanced ML technique known as Long Short-Term Memory (LSTM) networks to determine accurate energy demand forecast by capturing temporal dependencies and non-linear trends when consuming energy data. Using Simulation, we present model’s efficacy in achieving accurate forecast using Mean Absolute Percentage Error (MAPE) of 5.6%, a peak load reduction of 20%, and energy cost savings that exceeds 24%. We validate Computational efficiency with execution times that is better for real-time operation and grid scalability of 10,000 IoT devices. these results pave way for future research in hybrid forecast analysis, and multi-objective optimization. This can ensure stability of the grid in dynamic and decentralized energy landscape
PID Controller Tuning for an AVR System Using Particle Swarm Optimisation Techniques and Genetic Algorithm Techniques: A Comparison Based Approach Sabo Aliyu; Mahmud Bawa; Yunusa Yakubu; Alan Audu Ngyarmunta; Yunusa Aliyu; Alama Musa; Mohamed Katun
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36821

Abstract

This paper discusses tuning a Proportional-Integral-Derivative (PID) controller for an Automatic Voltage Regulator (AVR) system utilizing a particle swarm optimization technique and genetic algorithm. The primary objective is to compare the two methods. The AVR system was modeled and simulated using MATLAB, and the performance of the optimized PID controller was analyzed. The results demonstrate significant improvements in system performance with the metaheuristic-tuned PID controllers. Specifically, the GA-tuned PID controller achieved the best overshoot reduction (0.8%) and steady-state error minimization (0.0005), making it highly suitable for applications requiring precise voltage control. On the other hand, the PSO-tuned PID controller excelled in reducing settling time (2.7 seconds) and improving rise time (1.2 seconds), making it ideal for systems requiring rapid stabilization. Both metaheuristic approaches showed substantial enhancements. The study highlights the importance of selecting the appropriate optimization technique based on specific system requirements, whether the priority is minimizing overshoot, reducing settling time, or achieving near-zero steady-state error
Effect of Traditional and Modern Cooking Methods on the Microbial and Physicochemical Quality of Jollof Rice Nnenna Jennifer Omorodion; Olanrewaju Olubukola E
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36998

Abstract

Jollof rice is a popular West African dish, but its microbiological and physicochemical quality can be compromised by cooking methods. This study investigated the effect of traditional and modern cooking methods on the microbiological and physicochemical quality of Jollof rice sold around University of Port Harcourt. 20 samples of Jollof rice were collected from vendors, cooked using with firewood and gas cooker. Microbiological analysis and physicochemical analysis were conducted using standard methods. Microbiological analysis revealed that firewood cooked Jollof rice had higher total heterotrophic bacteria count (3.7×104 CFU/g to 4.5×104 CFU/g), exceeding stipulated standards. It was also observed that gas cooked Jollof rice had higher fungal count (1.4×103 CFU/g to 8×103 CFU/g) was within stipulated microbiological standard. Firewood-cooked Jollof rice had higher microbial counts compared to gas cooked Jollof rice. Five bacteria species: Bacillus subtilis, (18.75%); Klebsiella sp, (12.5%); Bacillus cereus, (18.75%); Staphylococcus aureus, (43.75%); and Proteus mirabilis, (6.25%) and three fungi species: Aspergillus niger, (57.1%); Fusarium sp, (14.3%) and Penicillum sp, (28.6%) were isolated. Physicochemical analysis showed that firewood cooked Jollof rice had higher values in; Ash, fibre, lipid and moisture content compared to gas-cooked Jollof rice. Gas-cooked Jollof rice had better physicochemical properties, including lower pH and higher protein content. This study concludes that Traditional cooking methods (firewood) may compromise the microbiological and physicochemical quality of Jollof rice, while modern methods (gas) produce safer and more nutritious products. This study highlights the importance of adopting safe cooking practices to ensure food safety and quality.
A Review on Battery Life and Energy Management in HWSNs using Adaptive Energy Harvesting Techniques Matthew Iyobhebhe; Abdoulie Momodou S. Tekanyi; K. A. Abubilal; Aliyu D. Usman; H. A. Abdulkareem; Yau Isiaka; E. E. Agbon; Elvis Obi; Chukwudi Ezugwu; Botson Ishaya Chollom; Abubakar Umar; Ajayi Ore-Ofe; Ridwan O. Eleshin; Fatima Ashafa; Paul Thomas Muge
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.37897

Abstract

This review article scrutinizes the limitations and improvements in battery life and energy management models within HSWNs that employ adaptive energy harvesting techniques. As Wireless Sensor Networks (WSNs) advance into various implementations, including monitoring environmental parameters and smart cities, effective energy management is vital. This review paper examines the responsibility of communication protocols, precisely routing and Medium Access Control (MAC) protocols, in optimizing energy efficiency. We scrutinize existing energy-efficient schemes and their comparative performance, thereby, concentrating on their adaptability to changing energy sources. Examining these protocols, we accentuate the best techniques and potential areas for development. Furthermore, the review highlights the necessity for advanced approaches integrating energy harvesting with resilience communication frameworks to maximize network lifespan. The discoveries are targeted to lead future research and advancement in the field of WSNs, hence, providing more reliable and efficient energy management schemes for WSNs.
Exploring the Role of AI and IoT in Production Efficiency, Quality, and Sustainability in Manufacturing Mustafa M. Mansour; Alaa M. Lafta; Azhar Mansoor Salman; Haider Sami Salman
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.38200

Abstract

This study explores the findings obtained through these research objectives, which will pave new agendas towards the medium-range economy renaissance, resilient digital ecosystems, and human-centered integrated intelligence. More in-depth exploration of the goal-specific research objectives awaits the research report. The main research gap and questions – and concomitant research approach, paradigm, and methodologies – framing the subsequent sections of the paper are substantiated by these objectives' delineation from the research questions. Practical implications and directions for prospective areas of techno-social innovation studies, building upon the findings, are outlined to conclude the paper. The aims, once accomplished, offer a symbiotic relationship with the research questions that catalyze interest in a domain that has hitherto been neglected mainly in Industry 4.0 literature. These aims become the guiding lights surmounting the destination of AIoT as a subversive innovation in developing and deploying discrete, reconfigurable, and near-continuous Industry 4.0 auxiliary open smart manufacturing.
Password Authentication Using Modify Multi-Connect Architecture Associative Memory Rusul Hussein Hasan; Inaam Salman Aboud; Rasha Majid Hassoon
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.38218

Abstract

Traditional password authentication systems often face challenges related to security vulnerabilities, slow authentication speeds, and susceptibility to noise interference. While previous methods such as Multi-Bias Associative Memory (MBAM) have made progress in improving authentication performance, they still suffer from inefficiencies in training time, accuracy, and robustness to noisy input data.To address these limitations, this research introduces an Enhanced Password Authentication System leveraging Modified Multi-Connect Architecture (MMCA) associative memory, supporting both graphical and textual passwords. MMCA enhances pattern recognition, enabling faster and more accurate authentication while ensuring robustness against noise interference. Compared to traditional methods, MMCA reduces computational overhead, improves resistance to adversarial inputs, and accelerates the authentication process.Experimental validation on 100 trials demonstrates 100% authentication accuracy for both graphical and textual password-based methods. The system achieves authentication times of 0.5 seconds for textual passwords and 1 second for graphical passwords, significantly outperforming existing solutions. Additionally, MMCA maintains reliable authentication even in the presence of up to 15% noise in graphical passwords. Comparative analysis shows that MMCA surpasses MBAM and other approaches in training efficiency and authentication speed, making it a promising solution for secure, fast, and noise-resistant user authentication.

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