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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 69 Documents
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Intelligent and Secure Package Receiver System Utilizing Internet of Things (IoT) Technology Handayani, Retno Dwi; Jamal, Zaidir; Alkahfiansyah, Muhammad; Rosmalia, Lia; H Sudibyo, Novi; Herwanto, Riko
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.38254

Abstract

The rapid expansion of e-commerce has increased the demand for secure and efficient package delivery solutions, particularly for recipients who are frequently away from home. This study presents the development of a Smart Package Receiver Box, an Internet of Things (IoT)-based system that integrates sensors and remote control functionalities to enhance package security. The system incorporates an ESP32-CAM microcontroller, an ultrasonic sensor for courier detection, a PIR sensor for package counting, and a solenoid door lock for secure access control. These components are integrated with the Telegram messaging application, enabling real-time notifications, visual monitoring, and remote control of package deliveries. Experimental testing demonstrates that the system reliably detects couriers within a 5 cm to 30 cm range, accurately counts inserted packages, and ensures a secure locking mechanism with prompt response times. The collected data confirm stable system performance, minimal delays, and effective remote accessibility for users. Despite its advantages, the system presents certain limitations that warrant further improvements. The current implementation lacks encrypted communication, posing potential security vulnerabilities that could be mitigated by integrating AES-256 encryption and secure authentication protocols. Additionally, the system's reliance on Telegram makes it susceptible to disruptions in service availability, necessitating the incorporation of alternative communication channels such as SMS notifications or cloud-based APIs. Future enhancements will focus on strengthening data security, increasing system redundancy, and conducting comprehensive field testing to improve robustness and scalability for broader adoption in modern package delivery systems.
Adaptive Resonance Theory-Based Approach for Robust and Efficient Face Recognition Zangana, Hewa; Khalid Mohammed, Ayaz; Omar , Marwan; Mahmood Mustafa, Firas; Vega Vitianingsih , Anik
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.38709

Abstract

In recent years, face recognition systems have gained significant traction due to their applications in security, surveillance, and user authentication. Despite the advances in deep learning techniques, challenges such as varying lighting conditions, occlusions, and facial expressions continue to affect the robustness and efficiency of these systems. This paper proposes a novel approach to face recognition based on Adaptive Resonance Theory (ART). ART's ability to adaptively learn and recognize patterns in a stable and incremental manner makes it particularly suitable for handling the dynamic variations encountered in face recognition tasks. Our proposed ART-based face recognition framework is evaluated on multiple benchmark datasets, demonstrating superior performance in terms of accuracy, robustness to noise, and computational efficiency compared to traditional methods. The experimental results highlight the potential of ART to enhance the reliability of face recognition systems in real-world applications.
Power Quality Enhancement Using Single Phase Shunt Active Filter Based ANFIS Supplied by Photovoltaic Fitriani, Anis; Amirullah, Amirullah; Bhumkittipich, Krischonme
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.39071

Abstract

This paper proposes a single-phase shunt active filter (ShAF) combined with photovoltaic (PV) to enhance power quality performance by reducing source current harmonics and compensating for reactive power in a single-phase 220-Volt distribution system with a frequency of 50 Hz connected to a non-linear load. The PV panel consists of several PV modules with a maximum power of 600 W each. An adaptive neuro-fuzzy inference system (ANFIS) controls the voltage in the DC link capacitor circuit in the ShAF. This method is proposed to overcome the weakness of the Fuzzy Sugeno method in neural-network-based learning capabilities to determine the fuzzy rules of the input membership functions (MFs) and the weakness of the proportional-integral (PI) control in determining proportional and integral constants using trial and error method. The single-phase system is connected to a non-linear load with a combination, i.e. without ShAF, using ShAF, and using ShAF-PV, respectively, with a total of seven cases. Based on the three proposed control methods and model configurations, the ShAF-PV circuit with ANFIS control is able to result in the best performance because it is able to produce the lowest source current THD. The single-phase system using ShAF-PV with ANFIS control is also capable of injecting the largest reactive power compared to the ShAF and ShAF configurations with PI and Fuzzy-Sugeno control. The increase in reactive power in the ShAF-PV is further able to compensate for the reactive power, so it is able to suppress and reduce the source reactive power significantly.
A Deep Learning Approach to Fake News Classification Using LSTM Andrianarisoa, Sitraka Herinambinina; Ravelonjara, Henri Michaël; Suddul, Geerish; Foogooa, Ravi; Armoogum, Sandhya; Sookarah, Doorgesh
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.39360

Abstract

The rapid spread of misinformation on digital platforms poses a major challenge today. The ability to detect false information is essential to mitigate the associated harmful consequences. This research presents a deep learning approach for detecting fake news using Long Short-Term Memory (LSTM) model, which captures linguistic patterns and long-term dependencies in text. Our approach consists of optimizing the model through different experiments based on hyperparameter tuning, on a pre-processed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves high accuracy of 0.9974, with embedding dimension of 128 using 100 LSTM units, batch size of 64 and drop-out rate of 0.48. It is a substantial improvement over previous studies. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a fine-tuned LSTM network with robust data preprocessing can provide a powerful tool to combat online misinformation.
Estimating Residential Natural Gas Demand and Consumption: A Hybrid Ensemble Machine Learning Approach Ajuji, Mohammed; Dawaki , Muhammad; Mohammed , Ahmed; Ahmad4, Abuzairu
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.40135

Abstract

The routine use of natural gas, particularly in residential settings, has been integral to human activities for many decades. This study proposes a hybrid ensemble regression machine learning model for forecasting residential natural gas demand. Accurate demand prediction is essential for efficient energy management and resource planning. The proposed approach integrates multiple regression algorithms including K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Linear Regression (LR) to leverage the strengths of each model and enhance overall predictive performance. The ensemble method operates in two phases: training individual regression models on the dataset, followed by aggregating their predictions. Model performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R²), and prediction accuracy, and is benchmarked against individual models. Cross-validation techniques were applied to ensure the robustness of the results. Experimental consequences demonstrate that the hybrid ensemble approach consistently outperforms standalone models by capturing diverse patterns and relationships within the data.
Adaptive Steganographic Technique For Digital Images Based On The Least Significant Bit Substitution Ghlai, Ekhlas Ghaleb Abdulkadhim; Zaman Mahdi Abbas; Muqdad Abdulraheem Hayder
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.40143

Abstract

It has become natural to retain most of the information electronically, due to developments and improvements in information and communication technology. Thus, information security has become a major problem. Aside from cryptography, two strategies could be utilized to share information in a secure manner. Cryptography and steganography are two of these mechanisms. Using an encryption key known to both the receiver and the sender, the message is encrypted. Without the encryption key, no one will be able to read the message. Consequently, we offer a method that is efficient and based on the Least Significant Bit (LSB) to address this problem. Improved dependability is attained by employing the LSB substitution approach, which reduces the embedding error rate. Utilizing LSB substitution in conjunction with MLEA, Secret Key (SK), transposition, and flipping, we present an algorithm for image-based steganography. Our suggested method is effective and produces efficient outcomes, as demonstrated by multiple experiments. We used a number of Quality Assessment Metrics (QAMs) to evaluate 125 unique RGB images with varying degrees of hidden information, including things like PSNR, Contrast, and Image Histogram (IH). In addition to security analysis, we compared our results to those of other approaches that were already in use and found that ours were superior. The result shows a considerable improvement over the advanced techniques currently available.
Control of DC Motor in Laundry Liquid Waste Treatment based on Esp32-S3 And Thingsboard Platform Bao, Benesiktus; Aribowo, Widi; Lukita Wardani , Ayusta; Chandra Hermawan, Aditya
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.41371

Abstract

Direct disposal of untreated laundry wastewater contributes to environmental pollution, with TSS (Total Suspended Solid) levels reaching 600 mg/L, far exceeding the 100 mg/L limit set by East Java Governor Regulation No. 72 of 2013. This research aims to develop an automated treatment system using an ESP32-S3 microcontroller integrated with pH, TSS, and temperature sensors, with real-time monitoring through the ThingsBoard platform. The DC motor serves as an actuator for the mixing and chemical feeding process. System testing showed the DC motor control had a 100% rate in processing TSS levels and 95% in reducing pH levels. On IoT data transmission, the average delay was 4 seconds for turbidity and 5 seconds for pH. Processing effectiveness was classified as 71% “Feasible,” 5% “Very Feasible,” and 19% “Less Feasible.” While there are some limitations, the system shows potential for adaptive wastewater treatment, which requires further improvements in sensor calibration and control reliability.
Design of a 120V, 5A SEPIC DC-DC Converter for Unipolar 120V DC Microgrid Obais, Abdulkareem Mokif; Mukheef, Ali Abdulkareem
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.44140

Abstract

SEPIC is a DC-DC converter that functions in both boost and buck modes, reducing voltage stress on active power switches. It is utilized in electric vehicles, marine vessels, and aircraft to minimize dimensions, mass, maintenance, and operational expenses while enhancing efficiency, safety, and dependability. DC microgrids, characterized by their straightforward topology and economical materials, provide enhanced efficiency relative to AC microgrids. The previous literatures concerning SEPIC converters and DC-DC microgrid’s applications are reviewed in this work. This paper presents a SEPIC-based DC-DC converter designed for direct connection to a unipolar 120V DC microgrid and capable of delivering 600W of DC power. It is outfitted with a current sensor and a protective switch to provide self-protection against microgrid disturbances, such as brief short circuits. The converter has been designed and evaluated using PSpice. The simulation results confirmed the design methods of the proposed converter and demonstrated effective protection against high current events.
Mapping the Future Skills: A Training Needs Analysis of Education Support Staff in a Vocational Faculty Hafidz, Abdul; Warju; Lestari , Yuni; Kusuma, Fajar Indra; Udhif Nofaizzi, Mafrur; Khusniya, Tri Wardati
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (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.v2i3.48473

Abstract

Educational staff, commonly referred to as administrative human resources, play a critical role as strategic assets in supporting the success of higher education institutions, including at the Vocational Faculty of Surabaya State University (Unesa). Institutional performance and service quality are closely linked to the competence and performance of these educational staff. Therefore, systematic training and development efforts are essential to enhance their competencies in line with the dynamic needs of the institution. One effective approach to designing relevant and impactful training programs is through Training Needs Assessment (TNA), which serves as a critical initial step in the training design process. TNA helps to identify gaps between existing competencies and future institutional demands, ensuring that training programs are targeted, responsive, and aligned with both institutional strategic objectives and individual performance improvement. This study aims to design a comprehensive training and development model for educational staff at the Vocational Faculty of Surabaya State University, based on a structured TNA process. The TNA will adopt both macro and micro approaches, mapping institutional-level needs as well as job-specific and individual performance gaps. The expected output is the development of an up-to-date, data-driven training and development model that can serve as a strategic reference for preparing faculty development plans and academic texts in the future, ensuring the sustainable improvement of educational staff competencies and institutional performance

Page 7 of 7 | Total Record : 69

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