cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Widi Aribowo
Contact Email
widiaribowo@unesa.ac.id
Phone
+62811307761
Journal Mail Official
vubeta@unesa.ac.id
Editorial Address
Jl. Prof. Moch Yamin, Ketintang, Kec. Gayungan, Surabaya, Jawa Timur 60231
Location
Kota surabaya,
Jawa timur
INDONESIA
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
Modelling and Simulation of Damping Controller in DFIG AND PMSG Integrated with a Convectional Grid: a Review Sabo Aliyu; Dauda Dahiru; Noor Izri Abdulwahab
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.v2i2.34749

Abstract

Wind energy conversion systems stand out as one of the most abundant sources of energy provided by nature. These systems are highly sustainable and environmentally friendly, as they do not generate pollution. Damping controllers are specifically designed to enhance the robustness and adaptability of hybrid systems utilizing permanent magnet and double-fed induction synchronous generators. These generators are carefully integrated with conventional energy sources, necessitating a vigilant focus on grid stability, particularly rotor angle stability. This stability is crucial for preventing mechanical oscillations and potential disruptions in the grid caused by instability. Furthermore, power system stabilizers with excitation systems are carefully designed and optimized to maximize damping performance while minimizing energy losses. In this context, damping controllers play a vital role.
A Review on Energy Consumption Model on Hierarchical Clustering Techniques for IoT- based Multilevel Heterogeneous WSNs Using Energy Aware Node Selection Matthew Iyobhebhe; Abdooulie Momodou. S. Tekanyi; K. A. Abubilal; Aliyu. D Usman; H. A. Abdulkareem; Yau Isiaku; E. E Agbon; Elvis obi; Ishaya Chollom Botson; Chukwudi Ezugwu; Ridwan. O. Eleshin; Fatima Ashafa; Saba Abubakar; Abubakar Umar; Ajayi Ore-Ofe; 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.v2i2.34882

Abstract

This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. The review article deliberates on the challenges posed by dynamic wireless sensor network conditions, node heterogeneity like energy-based, and scalability challenges that affect energy management. This review article scrutinizes the energy consumption model related to hierarchical clustering methods in IoT-based multi-tier heterogeneous networks (WSNs). Since energy efficiency is vital to prolong the operational activities of sensor nodes, this review article concentrated on energy-aware node selection as a significant technique for improving energy consumption. We scrutinize different factors affecting efficient node selection, comprising residual energy, transmission distance, and sensor node reliability while juxtaposing these techniques with traditional node selection schemes. Furthermore, the importance of developed modeling techniques was highlighted. Finally, future research directions were outlined, by accentuating the incorporation of energy harvesting and collective models to improve the stability and operation of Wireless Sensor Networks. This holistic overview aims to offer appreciated insights for authors and practitioners in WSNs.
Mathematical Modelling of Truck Platoon Formation Based on a Dynamic String Stability Ore Ofe Ajayi; Abubakar Umar; Ibrahim Ibrahim; Lawal Abdulwahab Olugbenga; Ajikanle Abdulbasit Abiola
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.v2i2.34941

Abstract

In this research, developinga Fuzzy Logic Cooperative Adaptive Cruise Control (FCACC) scheme significantly enhanced truck platooning string stability by ensuring rapid stabilization and robustness against disturbances. The mathematical model designed and implemented in SUMO/OMNeT++ simulated various scenarios, demonstrating the superiority of the FCACC over conventional CACC, PATH CACC, and Ploeg CACC controllers. Quantitatively, the FCACC achieved velocity and spacing stability within an average of 7.33 seconds and 4.39 seconds using the triangular-centroid method, outperforming the CACC, PATH CACC, and Ploeg CACC by 28.09%, 25.21%, and 22.26% for velocity stability and 31.69%, 29.96%, and 28.01% for spacing stability, respectively. Additionally, the FCACC reduced the Expected Arrival Time (EAT) deviation by 4.62% compared to the CACC, demonstrating its efficiency in handling disturbances such as truck breakdowns. The FCACC's rapid stabilization, even in the presence of impulse signal disturbances, was evident in its ability to recover within 2.3 seconds for speed and 3.6 seconds for distance, compared to 27.5 seconds and 10.1 seconds for CACC. The fuzzy-PLEXE framework further emphasized the FCACC’s advantage by inducing more minordistance errors and faster stability times than other models, achieving stability in 53 seconds versus 60 seconds for Ploeg CACC. These results underline the FCACC’s efficacy in mitigating unexpected disruptions and maintaining optimal string stability. However, limitations such as dependency on precise sensor data, susceptibility to communication delays, and challenges with scalability for larger platoons were observed, suggesting areas for future optimization.
Impact of Grid-Scale Solar Photovoltaic Integration on Power System Performance Sunday Ugwuanyi, Nnaemeka; Ugwuoke, Nestor Chima; Obi, Patrick Ifeanyi
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.v2i2.35474

Abstract

The impact of SPV integration on grid performance is a topic of ongoing debate, with conflicting reports on its effects. This study employs modal analysis, Newton-Raphson power flow, and time-domain simulations to assess the effects of SPV integration on voltage profiles, active power loss, and system stability in the IEEE 4-machine and Nigerian 50-bus power systems. The findings reveal that SPV integration impacts power systems differently, emphasizing the need for a comprehensive approach that considers voltage stability, power losses, and stability constraints. While SPV integration can improve voltage levels and reduce power losses, it may also compromise transient stability, highlighting the importance of careful planning and grid reinforcement. For the IEEE 4-machine system, SPV integration is feasible up to 25% based on power loss, but transient stability constraints limit it to 0%. For the Nigerian grid, optimal SPV integration is achieved at 10% based on power loss and voltage profile, while transient stability constraints limit integration to 5%. This study underscores the necessity of a multi-metric approach to defining SPV penetration limits, considering the trade-offs between voltage performance, power loss, and system stability.
Solar-Powered IoT-Based Home Fire Early Warning and Protection System Muhammad Arief Wicaksono; Amirullah Amirullah; Boonyang Plangklang
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.v2i2.35773

Abstract

This paper presents the implementation of a prototype of a fire early warning system in a residential house using temperature and smoke sensors supplied by an Internet of Things (IoT) based solar module. The 10 Wp solar module is the energy source connected to a 12V battery via a solar charge controller (SSC). Data retrieval is carried out through testing by the MQ-2 Sensor and LM35 Sensor, respectively, to detect smoke (gas) and heat. The system then activates the buzzer, sends data from the detection of the status and level of smoke (gas) and heat to the smartphone screen and liquid crystal displays (LCD) in the form of an alarm, and orders the PLN switch to work to cut off the electricity. The results of the tool test show that the proposed prototype is able to provide early warning notifications regarding the status and level of smoke (gas) and heat - both from the LCD and remotely from the smartphone, and is able to activate the relay dan order the switch cuts off the electricity to prevent fire. The prototype system's source is supplied by solar modules independently, making it applicable in remote areas with limited electricity access-compared to the previous model which was supplied solely by the electricity grid.
Microbiological and Physiochemical Assessment of Corn Meal (Agidi) Nnenna Jennifer Omorodion; Modebola Victoria OlaIokungbaye
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.v2i2.35907

Abstract

Corn meal (Agidi) is a gel-like traditional fermented starchy food item produced from maize, although millet and sorghum can also serve as raw materials. It is known by different names in different localities. A total of 30 corn meal (Agidi) from differentsellers from various communities and comprisingof 15 white and 15 jollof agidi samples fromChoba market in Port Harcourt Rivers State were examined and analyzed using standard microbial techniques. The Total Bacteria count for white (plain) Agidi rangedof 6.30-8.06 logcfu/g. The Staphylococci count for White Agidi samples ranged of6.0–8.2 logcfu/g. The Coliform count ranged of 6.00-7.96 logcfu/g. The results generated from this study exceeded the permissiblelimit for bacteria in food. Bacteria isolated from White agidi include Staphylococcus spp (31.58%) and Enterococcus sp (21.05%). Bacillus sp, (18.42%), Escherichia coli (15.75%) and Klebsiella sp (10.53%). Pseudomonas sp (2.63%). For jollof agidi, the bacterial isolated Staphylococcus spp (30.8%) Bacillus spp (24.6%). Enterococcus sp (20.0%), Escherichia coli (12.3%), Klebsiella sp 7(10.8%) and Pseudomonas sp (1.5%). pH of corn meal ranged from 4 –6, the moisture content ranged from 80% –90%, while the titratable acidity ranged of0.20 –0.40. Proper handling of agidi during production must be taken.
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.

Page 1 of 2 | Total Record : 20

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026): (In Progress) Vol. 3 No. 1 (2026) Vol. 2 No. 3 (2025) Vol. 2 No. 2 (2025) Vol. 2 No. 1 (2025) Vol. 1 No. 3 (2024) Vol. 1 No. 2 (2024) Vol. 1 No. 1 (2024)