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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 95 Documents
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A Survey on Categorization of Threat Intelligence and Trust-based Sharing Strategies on Cyber Attack Nureni, Azeez; Tajudeen, Abdulquadri
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.v2i2.36031

Abstract

Threat Intelligence (TI) refers to knowledge derived from analyzing current and potential cyber threats, including their context, mechanisms, and indicators of compromise. By understanding adversaries ' tactics, techniques, and procedures, TI empowers organizations to proactively detect, prevent, and counter cyber threats. Given cyberattacks' increasing frequency and sophistication, stratifying and categorizing TI remains challenging, particularly in building trust for secure information sharing among organizations. This research addresses these challenges through a survey on TI categorization and trust-based sharing mechanisms. The study is expository researchthat employs quantitativeresearch methodology. The study incorporates a systematic literature review to explore TI classification, methodologies, and its effectiveness in mitigating cybersecurity vulnerabilities. Findings reveal that organizations leveraging advanced TI methods, such as machine learning and behavioral analytics, achieve up to a 60% reduction in threat detection and response times. Furthermore, trust-based sharing initiatives such as Information Sharing and Analysis Centers (ISACs) and standardized frameworks like Structured Threat Information eXpression (STIX) and Trusted Automated eXchange of Indicator Information (TAXII) enhance collaborative defense capabilities by 65%. The study concludes that integrating standardized sharing protocols, advanced analytics, and machine learning can significantly bolster cybersecurity defenses. It recommends global standardization of TI practices, incentivizing participation in information-sharing communities, and investing in workforce training to optimize TI deployment. These findings allow practitioners, policymakers, and researchers to strengthen cybersecurity frameworks.
Comparison Feed Forward Back Propagation Networks (FFBPNs) with Support Vector Machine (SVM) for Diagnosis Skin Cancer Based on Images Jawad, Rawaa; Jawad, Raheel
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.v2i2.36117

Abstract

Skin cancer is a type of malignancy responsible for 70 percent of overall skin cancer-related death worldwide. The purpose of the research is to use AI to detect skin cancer of all types more quickly and improve the efficiency of diagnostic radiology.The method used in this paper is an artificial neural network implemented for the detection of skin cancer and the watershed segmentation method for segmentation. The features extracted are shape and Gray-Level Co-Occurrence Matrix. The extracted feature is used for classification. The classifiers are Support Vector Machine and Feedforward Back Propagation applied in a Matlab environment and an image processing technique on a set of photographs that were collected from several websites, including the Kaggle web. The implementation of code for the detection of skin cancer by using data as 100 images 50 no cancer and 50 is cancer, the result shows a successful implementation for the detection of cancer in FFBP classifier a 45 and 2 is bad detection, as well as in SVM classifier 49 with 1 is bad diagnostic. The Conclusion shows SVM classifier provided results for the skin lesions classification produced 98% accuracy and the accuracy of the FFBP of 96 %. The conclusion of this study is helping people with skin cancer undergo a CT scan. The scan is tested using a computer trained to analyze CT scan data.
Early Heart Disease Prediction Using Data Mining Techniques Dugguh Sylvester Aondonenge; Ajayi Ore-Ofe; Kamorudeen Hassan Taiwo; Abubakar Umar; Isa Abdulrazaq Imam; Dako Daniel Emmanuel; Ibrahim Ibrahim
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.v2i2.36735

Abstract

Heart disease is a leading cause of mortality worldwide, characterized by the buildup of plaque in the arteries, which can lead to severe cardiovascular complications. Predicting heart disease is complex due to the need to analyze multiple risk factors, such as age, cholesterol, and blood pressure. This study develops a predictive model for earlyheart disease detection using data mining techniques to enhance timely and accurate diagnosis. The model combines multiple machine learning timely and accurate diagnosis. The model combines multiple machine learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach to improve prediction accuracy and reliability. The methodology follows five phases: data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The hybrid model was trained and tested using an 80:20 data split and evaluated against various classification algorithms. It achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, outperforming individual models. These results highlight the effectiveness of the hybrid approach in supporting early interventionfor heart disease, particularly in healthcare settings with limited diagnostic resources. This study demonstrates that advanced data mining techniques provide a viable solution for improving patient outcomes through the early detection of heart disease.
Simulation and Experimental Evaluation of a 5-Level Cascaded H-Bridge Inverter Mahmud Ismaila; Sulaiman Haruna Sulaiman; Ibrahim Abdulwahab; Ibrahim Abdullahi Shehu; Musa Mohammed
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.v2i2.37172

Abstract

Multilevel inverters (MIs) are designed in such a way that different DC sources could be used to achieve the desired output voltage. This includes high quality output voltage, reduction of voltage stress on the switches, low common mode voltages, better harmonic content and reduction in total harmonic distortion compared to the conventional voltage source inverter. As there will be reduction in THD the size of the filter will also get minimized which decreases complexity of the system. Sinusoidal pulse width modulation technique is commonly employed in MIs in order to obtain undistorted output voltage by eliminating lower order harmonics. Cascaded H-bridge MIs are the most preferable for this purpose due to their modularity, reliability, less usage of clamping diodes and ease of control of circuitry and it also reduces the switching and conduction losses of the system. In this study, 5-level cascaded H-bridge inverter was simulated in MATLAB/Simulink software environment. A prototype of the simulated 5-level inverter was also constructed and the result was compared with that obtained from simulation. The results of both the simulation and experimental measurement have the similar output voltage waveform and the THD value of 33.12% and 33% for the simulation and experiment respectively.
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

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