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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
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Articles 25 Documents
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Search results for , issue "Vol. 7 No. 3 (2025): September" : 25 Documents clear
Microcontroller-based Prototype Model of a Solar Wireless Electric Vehicle-to-Vehicle Charging System with Real-Time Battery Voltage Monitoring Priyadarshini, M. S.; Mahmoud, M. Metwally; Sur, Ujjal; Ardjoun, Sid Ahmed El Mehdi; Hysa, Azem; Bessous, Noureddine; Metwally, Khaled A.; Anwer, Noha
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13232

Abstract

The increasing adoption of electric vehicles (EVs) necessitates sustainable and efficient charging solutions, particularly in remote areas and emergency situations where conventional grid-based charging stations are inaccessible. This research presents an Arduino-based prototype model of the Solar Wireless Electric Vehicle-to-Vehicle Charging System (SWEV2VCS), integrating a TP4056 charging module, a microcontroller, and wireless power transfer (WPT) coils to facilitate efficient, autonomous charging. The system harvests solar energy through high-efficiency photovoltaic (PV) panels, which is then regulated and stored in lithium-ion batteries. The TP4056 module ensures safe and controlled charging by providing overcharge, over-discharge, and current regulation for battery protection. An Arduino-based microcontroller unit (MCU) is implemented to monitor and optimize power management, ensuring effective energy distribution and preventing inefficiencies. Wireless power transfer is achieved using electromagnetic resonance coupling, which enhances transmission efficiency over short distances. The system employs primary and secondary copper coils designed for resonant inductive coupling, enabling energy transfer between EVs without requiring a physical connection. The design and implementation include real-time battery voltage monitoring using an Arduino Nano and an I2C-based LCD display. The microcontroller measures battery voltage from an analogy pin, processes the data, and displays it on the LCD screen. The voltage sensing mechanism employs analogy-to-digital conversion (ADC) to ensure accurate readings. The LCD module provides real-time updates, enhancing user interaction and monitoring efficiency. The experimental setup verifies system functionality by continuously displaying voltage readings, facilitating better power management during wireless charging. This prototype serves as a fundamental step toward the development of automated, real-time monitoring systems in wireless EV charging applications.
Comparative Performance Analysis of LQR Based PSO and Fuzzy Logic Control for Active Car Suspension Abougarair, Ahmed; Aburakhis, Mohamed; Bakouri, Mohsen; Ma’arif, Alfian
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13237

Abstract

This study proposes a diffrent control strategy for active car suspension systems, comparing the performance of Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and fuzzy PD controller in optimizing ride comfort and handling. These methods were selected for their complementary strengths: PID for simplicity and industrial adoption, LQR for optimality in handling trade-offs between ride comfort and suspension travel, and fuzzy PD for adaptability to nonlinearities and road disturbances. A 4-DOF quarter-car model is employed to simulate vehicle dynamics, with road disturbances modeled as step and sinusoidal inputs. The PID controller is tuned using built-in tools such as the PID tuner app, while the LQR’s weighting matrices (Q and R) were optimized offline using PSO. The optimized weights were then substituted into the algebraic Riccati equation to derive the final feedback control gains, ensuring optimal performance while adhering to classical LQR theory. For the fuzzy PD controller, membership functions and rule bases are designed to adaptively adjust gains under varying road conditions. Simulation results demonstrate that the PSO-tuned LQR and fuzzy PD controllers outperform conventional PID by reducing body vertical displacement by 61% and 23%, respectively, and overshoot by 75% (fuzzy PD) and 60.2% (LQR) under step excitation. The LQR controller based PSO also shows superior adaptability to stochastic road inputs and minimizing the control signal by 83.3% compared to PID. By integrating PSO-based LQR gain optimization and adaptive fuzzy logic, this work advances active suspension control, offering a quantifiably superior alternative to classical approaches. This study contributes to the technological development of the automotive world in order to provide comfort and safety for the passenger under different conditions, which contributes to the design of more comfortable vehicles with better performance in the future.
A Comprehensive Review of Optimization Techniques in Industrial Applications: Trends, Classifications, and Future Directions Asih, Hayati Mukti; Mohamad, Effendi; Irianto, Irianto; Ma’arif, Alfian
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13261

Abstract

In recent years, optimization techniques have played a central role in enhancing operational efficiency and decision-making across diverse industrial sectors, including manufacturing, logistics, and transportation, energy, healthcare, and agriculture. These sectors face complex, large-scale, and often nonlinear challenges that demand both precision and adaptability. The research contribution of this review is to provide a structured classification of optimization methods—namely exact algorithms, heuristics, metaheuristics, and AI-integrated hybrid models—and to critically evaluate their practical applications, limitations, and emerging trends across industries. This study adopts a review approach to identify and compare those techniques in solving various optimization problems. Through a detailed analysis of over 30 recent publications for last four years, the review highlights how these techniques are being applied in real-world industrial environments, including cold chain logistics, smart energy systems, precision agriculture, and healthcare scheduling. The results indicate a growing reliance on hybrid and AI-enhanced models due to their superior scalability, adaptability, and potential alignment with Industry 4.0 and Sustainable Development Goals (SDGs). However, challenges remain in areas such as computational efficiency, model interpretability, and real-time data integration. In conclusion, this study provides valuable insights for both researchers and practitioners seeking to apply optimization techniques more effectively in industrial systems, while also identifying critical research gaps for future exploration by addressing the growing complexity and sustainability demands of modern industry.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Understanding Time Series Forecasting: A Fundamental Study Furizal, Furizal; Ma’arif, Alfian; Kariyamin, Kariyamin; Firdaus, Asno Azzawagama; Wijaya, Setiawan Ardi; Nakib, Arman Mohammad; Ningrum, Ariska Fitriyana
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13318

Abstract

Time series forecasting plays a vital role in economics, finance, engineering, etc., due to its predictive power based on past data. Knowing the basic principles of time series forecasting enables wiser decisions and future optimization. Despite its importance, some researchers and professionals find it difficult to use time series forecasting techniques effectively, especially with complex data settings and selection of methods for a particular problem. This study attempts to explain the subject of time series forecasting in a comprehensive and simple manner by integrating the main stages, components, preprocessing steps, popular forecasting models, and validation methods to make it easier for beginners in the field of study to understand. It explains the important components of time series data such as trend, seasonality, cyclical components, and irregular components, as well as the importance of data preprocessing steps, proper model selection, and validation to achieve better forecasting accuracy. This study offers useful material for both new and experienced researchers by providing guidance on time series forecasting techniques and approaches that will help in enhancing the value of decision making.
Modified Orca Algorithm Based on the Navigation Behavior for Optimal Unit Commitment in Power Systems Widayanti, Lilis; Afandi, Arif Nur; Herwanto, Heru Wahyu
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13645

Abstract

This study presents the Novel Navigation Orca Algorithm (NNOA), an innovative optimization algorithm derived from Orca Algorithm (OA). NNOA addresses the unit commitment (UC), a complex issue in power systems that focuses on scheduling generator units to meet power demand while taking into account each generator's limitations, with the goal of lowering operating costs and gas emissions. NNOA exhibits orca hunting behavior through echolocation, utilizing the Doppler effect principle to promote adaptive movement and circumvent local optima, as in contrast to OA's wave-based exploration. The algorithm was evaluated utilizing IEEE 30-bus system data, focused on the Integrated Economic and Emission Dispatch (IEED) objective. The performance was evaluated against OA and Particle Swarm Optimization (PSO) through convergence analysis over 10 and 30 trials, each consisting of 100 iterations. NNOA decreased the IEED value by 1.33% in regard to OA and 1.51% in regard to PSO. NNOA achieved convergence in 10 iterations, whereas OA required 35, indicating 71.4% faster convergence rate. Wilcoxon rank-sum tests demonstrated significant differences between NNOA, OA, and PSO pairings. NNOA's per-iteration computation time exceeds the time needed by PSO, but it remains economical and profitable. Significantly, NNOA contributes minimizing the fuel consumption and emissions cost, which has a positive environmental impact. It effectively adheres to the required constraints, which include the hourly power demand and generator output limits. Future research is encouraged to apply NNOA to larger-scale power systems and explore its hybridization with PSO to enhance computational efficiency, result consistency, and robustness in practical grid operations.
A Blockchain-Enabled Internet of Things Framework for Enhancing Trust and Privacy in Social Work Case Management Chen, Yih-Chang; Lin, Chia-Ching
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13653

Abstract

Traditional social work case management systems face critical challenges including data silos, security vulnerabilities, and insufficient inter-agency collaboration, limiting service efficiency and compromising client privacy protection. This study addresses these challenges by developing and evaluating a novel technological framework that integrates blockchain consortium networks with Internet of Things (IoT) devices to establish multi-party trust mechanisms and enhance service delivery. The research contribution is a comprehensive four-layer system architecture featuring 28 smart contracts, decentralized trust mechanisms, and privacy-preserving technologies including homomorphic encryption and differential privacy for social work applications. The methodology employed a mixed-methods approach involving system design and development, followed by a six-month pilot implementation across three social work institutions in Taiwan with 249 participants. Data collection encompassed quantitative performance metrics from system logs and IoT sensors, alongside qualitative feedback through interviews and focus groups. The blockchain network achieved 850 transactions per second with 99.2% system availability, significantly outperforming industry standards. Results demonstrated substantial operational improvements: 37.1% reduction in case processing time, 87.3% increase in service efficiency, and 26-fold increase in inter-agency collaboration frequency. The blockchain-based trust mechanism increased inter-agency data sharing willingness from 61.3% to 84.6%, while maintaining 100% anonymization coverage with 91.3% analytical accuracy. Cost-benefit analysis revealed a 2.8-year payback period with 41.2% return on investment. This research demonstrates the feasibility and effectiveness of blockchain-IoT integration in social work, providing a practical framework for digital transformation while ensuring data security and privacy protection in sensitive social service environments.
Solar-Powered EV Charging Using Modified SEPIC-Luo Converter with Recurrent Neural Network Technique Sreedevi, S. L.; Geetha, B. T.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13675

Abstract

In order to address issues with renewable energy utilization processes and the growing power consumption of Electric Vehicles (EVs) in the near future,a solar powered charging station for EV is developed. Initially, a highly efficient Modified Single Ended Primary Inductor Converter (SEPIC) Luo converter is used to increase a low voltage of the PV system. The maximum power of the Photovoltaic (PV) system is then tracked using the Recurrent Neural Network based Maximum Power Point Tracking (RNN-MPPT), whose parameters are adjusted using the Monarch Butterfly Optimization (MBO) algorithm. Then, the high frequency full bridge inverter effectively transforms the power and isolation transformer is utilized for decreasing electrical noise and interference. Furthermore, the interleaved synchronous rectifier is used for attaining effective charging by reducing conduction losses. The developed work is applied in Matlab/Simulink software, reveals that the developed work attains the converter efficiency of 97.44%when compared to 90% of Luo and 95.16% of Enhanced SEPIC, ensuring the stable and reliable power delivery. Also, The MBO-RNN approach exhibits 98.8% of tracking efficiency and a root mean square error of 0.0125.
A New Adaptive Flux-Oriented Control Framework for Induction Motors with Online Neural Network Training Bekhiti, Belkacem; Al-Sabur, Raheem; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13727

Abstract

Unlike conventional field oriented control methods, this paper presents a mathematically novel control strategy for induction motor drives, formulated using a two-loop nonlinear dynamic inversion (NDI) framework inspired by aeronautical control architectures. Sensorless operation is realized with a conventional rotor flux observer, while several additional enhancements are introduced to raise overall performance. In particular, a real time radial basis function (RBF) neural network is systematically embedded in a model reference adaptive system (MRAS), replacing the traditional PI adaptation loop with an online training mechanism that improves speed estimation accuracy under parameter variations and load disturbances. The single layer RBF network is trained by gradient descent and incorporated into the nonlinear observer without compromising closed loop stability. The complete controller was implemented on a 1.1 kW, 1430 rpm induction motor using a dSPACE DS1104 real time platform. Experimental results show clear superiority over classical FOC as well as DTSFC and DTRFC schemes, achieving the lowest measured flux ripple (0.002 Wb), minimal torque ripple (0.043 N·m), and the fastest torque response time (0.65 ms). The steady state speed error was reduced by 91 % (from 0.65 to 0.08 rad/s), settling times remained below 60 ms, and both RMSE and ISE metrics decreased appreciably across all tested conditions. Although the proposed design incurs moderate computational overhead, it is fully compatible with real time execution. Future work will examine scalability to high power drives, improved resilience to temperature induced parameter drift, and adaptation of the NDI based framework to permanent magnet machines.
Sensor Fusion of Laser and Inertial Units with Kalman-KMeans-Fuzzy Framework for Real-Time Railway Geometry Monitoring Fikri, Ahmad Atif; Subhan, Muhammad Ferindin Nuha; Suryanto, Heru; Muhdi, Krisna Dwipa; Pratama, Daniel Febrian; Iqbal, Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13780

Abstract

Maintaining railway track geometry integrity is essential to ensuring transportation safety and predictive maintenance. Conventional manual inspection methods are limited by low sampling frequency, subjective interpretation, and delayed anomaly detection. This study introduces a real-time, embedded monitoring system using VL53L0X infrared laser sensors and an MPU6050 IMU to measure gauge, cross-level height, and inclination. Sensors are mounted on a lightweight aluminum trolley and sampled every 0.5 seconds using an Arduino-based platform. A Kalman Filter reduces measurement noise, with tuned covariance matrices based on field calibration. Filtered outputs are clustered via K-Means (K = 2), validated by the Elbow Method and Silhouette Score (>0.6). Maintenance categories are assigned through a fuzzy logic system, with a ±1 mm sensitivity analysis confirming >85% decision stability. Field results demonstrate a measurement noise, achieving RMSE and MAE values of 0.8165 mm and 0.3175 mm for gauge and height, and 0.3086° and 0.0952° for inclination, respectively and a SNR gain from 0.5 dB to 21.7 dB. The low-cost, modular setup supports scalable, condition-based maintenance and demonstrates robustness in noisy environments. This approach offers a practical foundation for future integration with predictive analytics and digital twin technologies in smart rail infrastructure.

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