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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.
Arjuna Subject : -
Articles 295 Documents
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Enhancement of Channel Estimation in Spectrally Efficient Frequency Division Multiplexing-based Massive MIMO Systems for 5G NR and Beyond: A Comparative Analysis of LSE, MMSE, and Deep Neural Network Architectures Kadhim, Esraa Hadi; Abdulsadda, Ahmad T. Abdulsadda
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13058

Abstract

Channel estimation is a significant challenge in 5G NR and future communication systems because of complicated propagation settings, including high-order modulation and Nakagami-m fading. High spectral efficiency is necessary to satisfy increasing data demands. To improve channel prediction in a 32×32 Massive MIMO architecture, this study suggests a unified framework that combines Deep Neural Networks (DNN), compressed pilot signals, and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The system utilizes SNR as an input characteristic for the deep learning model and 256-QAM modulation. With average MSE (Mean Square Error) values of 1.2776 for LSE (Lest Square Estimation ) and 1.055 for MMSE (Minimum Mean Square Error ), simulation findings show that traditional estimators like LSE and MMSE perform well in moderate-SNR settings. The average MSE of 0.498 obtained by the DNN-based estimator is much lower and best. The model's advantage in capturing nonlinear channel features is shown by graphical comparisons of real vs. anticipated channel gains, leading to better reliability and throughput. In summary, the DNN model exhibits exceptional performance and versatility for real-time channel prediction in spectrally efficient next-generation communication systems, e.g., IoT, autonomous systems.
Induction Motor Speed Control Using PID Tuned by Particle Swarm Optimization Under Vector Control Maghfiroh, Hari; Sulistyo, Meiyanto Eko; Ma’arif, Alfian; Raharja, Nia Maharani; Suwarno, Iswanto; Wati, Dwi Ana Ratna; Baballe, Muhammad Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13112

Abstract

Induction motors (IMs) are widely used in industrial applications due to their cost-effectiveness, durability, and low maintenance requirements. This study investigates the speed control of an induction motor using vector control combined with a PID controller whose parameters are tuned via Particle Swarm Optimization (PID-PSO). A reduced-order small-signal state-space model is derived from a detailed nonlinear IM model to facilitate efficient controller tuning while maintaining fidelity to real-world behavior. The PID parameters are optimized using PSO, with the Integral of Absolute Error (IAE) selected as the objective function due to its ability to penalize long-duration deviations and reflect steady-state performance. The optimized PID controller is then validated on the full nonlinear IM model under speed and load variations. Simulation results demonstrate that PID-PSO significantly outperforms manually tuned PID control in terms of tracking accuracy, reducing IAE by 37.79% and 14.76% under speed and load variation conditions, respectively. However, this improvement comes at the cost of slightly slower settling time. These results highlight a trade-off between accuracy and transient response, motivating future research on multi-objective optimization to balance conflicting criteria such as robustness, energy efficiency, and response time.
Performance Enhancement of Photovoltaic Panels Using Passive Heatsink Cooling and Single-axis Solar Tracking Apribowo, Chico Hermanu Brillianto; Winda, Wiwik Nur; Maghfiroh, Hari; Iftadi, Irwan; Baballe, Muhammad Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13150

Abstract

Indonesia's persistent tropical climate and strong sunlight year-round lend themselves well to photovoltaic (PV) applications. However, prolonged sun exposure raises panel temperatures and reduces energy conversion efficiency. This study examines how to experimentally enhance the power output and efficiency of PV systems by combining single-axis solar tracking with passive heatsink cooling. On sunny days, two identical 50 W polycrystalline PV panels were evaluated in Surakarta, Indonesia. Four setups were tested: baseline (no tracking or cooling), tracking only, cooling only, and a combination of both. Temperature, voltage, and current data were gathered using calibrated INA219 and MLX90614 sensors. Results indicate the system can enhance efficiency and power output. Tracking alone improved power by 26.42% and efficiency by 2.16%; cooling using an aluminum heatsink boosted power by 40.28% and efficiency by 3.39%. Combining tracking and cooling yielded the highest power increase of 55.61%, with a 2.79% efficiency gain. These findings demonstrate the reduced efficiency benefits due to thermal effects despite higher irradiance in tracking systems. This research offers practical insights for optimizing PV performance in tropical regions and supports developing cost-effective, hybrid enhancement strategies.
A Review on Smart Distribution Systems and the Role of Deep Learning-based Automation in Enhancing Grid Reliability and Efficiency Neda, Omar Muhammed
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13152

Abstract

The increasing complexity of modern power distribution systems has accelerated the need for advanced automation solutions to maintain grid reliability and efficiency. smart distribution systems (SDS), integrating distributed energy resources (DERs), internet of things (IoT) technologies, and advanced data analytics, are reshaping the conventional grid into a flexible and intelligent network. This review focuses on the application of deep learning (DL) techniques in enhancing automation within SDS, highlighting their role in key tasks such as anomaly detection, fault location, load forecasting, outage estimation, and customer clustering. Five DL models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, deep neural networks (DNNs), autoencoders, and hybrid models, are evaluated using synthetic datasets that approximate real world grid behavior. Acknowledging the limitations of synthetic data, this review emphasizes the need for future validation using empirical datasets and adaptive learning techniques. Performance trends are qualitatively compared across models and tasks, with observations such as suitability of LSTMs for time series forecasting and CNNs for localized event detection. Challenges including data quality, computational costs, and implementation constraints are discussed, along with potential mitigation strategies such as lightweight model architectures and explainable artificial intelligence. A comparative perspective with traditional machine learning and physiscs-based models is also provided to highlight the unique advantages and tradeoffs of DL methods. The findings undescore the potential of DL in SDS automation while outlining key areas further research and real-world deployment.
Time-domain Simulation and Stability Analysis of a Photovoltaic Cell Using the Fourth-order Runge-Kutta Method and Lyapunov Stability Analysis Priyadarshini, M. S.; Ardjoun, Sid Ahmed El Mehdi; Hysa, Azem; Mahmoud, Mohamed Metwally; Sur, Ujjal; Anwer, Noha
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13233

Abstract

This paper aims to analyze the nonlinear dynamic behavior of a photovoltaic (PV) cell under constant irradiance using numerical simulation and stability analysis. PV systems are inherently nonlinear and time-varying, making accurate dynamic modeling essential for control and performance optimization. Understanding how the system responds over time is critical for designing stable and efficient PV-based energy systems. A single-diode equivalent circuit model is used to represent the PV cell. The fourth-order Runge-Kutta (RK4) method is chosen for time-domain simulation due to its balance between computational efficiency and accuracy. A quadratic Lyapunov function is formulated to assess system stability by observing the sign of its time derivative. Simulation results show that the voltage reaches steady state smoothly with minor overshoot, and the current converges rapidly. The Lyapunov function decreases consistently, confirming asymptotic stability. The system demonstrates a maximum voltage error below 2% and low standard deviation, with consistent return to equilibrium despite changes in initial conditions. In conclusion, the proposed approach effectively characterizes the PV cell’s nonlinear dynamic behavior and confirms system stability under constant irradiance. The effectiveness of combining RK4 integration with Lyapunov analysis for modeling nonlinear PV dynamics ids demonstrated.
A Hybrid LSTM-CNN Approach Using Multilingual BERT for Sentiment Analysis of GERD Tweets Mekonnen, Atinkut Molla; Munaye, Yirga Yayeh; Chekol, Yenework Belayneh
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13281

Abstract

Analyzing public sentiment through platforms like Twitter is a common approach for understanding opinions on political matters. This study introduces a deep learning sentiment analysis model that integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to assess attitudes toward the Grand Ethiopian Renaissance Dam (GERD). LSTM is utilized to capture long-range dependencies in text, while CNN identifies significant local patterns. An initial dataset of 30,000 unlabeled tweets was collected in 2024 G.C., out of which 17,064 were labeled as positive, negative, or neutral. The labeled tweets were divided into 13,112 for training and the remaining for testing. The hybrid LSTM-CNN model demonstrated superior performance compared to the standalone models, delivering more accurate and balanced sentiment classification. A major feature of this study is the analysis of tweets written in Amharic, Arabic, and English. The model was trained over 35 epochs with a batch size of 46 and a learning rate of 0.001. Using multilingual BERT (mBERT) embeddings notably enhanced the model’s performance, with training and testing accuracies reaching 95.3% and 92%, respectively. The hybrid model also achieved a precision, recall, and F1-score of 90%. In a focused analysis of Arabic tweets, 3,710 were negative, 9,793 positive, and 4,814 neutral. These results emphasize the influence of linguistic diversity and class distribution on classification performance. While mBERT showed strong results, addressing class imbalance and expanding language-specific features remains crucial for further improvements.
PHUA: A Phone-handling User Algorithm Inspired by Human Mobile Usage Behavior for Global Optimization Zhang, Jincheng; Jantakoon, Thada; Laoha, Rukthin; Limpinan, Potsirin
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13407

Abstract

In this paper, we propose a new meta-heuristic algorithm, the Phone Operator User Algorithm (PHUA), based on the behavioral patterns of human mobile phone usage. The algorithm mimics the behavioral strategies that humans use to decide when and how to respond to mobile phone notifications. By simulating strategies such as perception triggering, priority evaluation, delayed response, mandatory inspection, do not disturb, and rest, the balance between exploration and exploitation in the global search process is optimized. We evaluate the performance of PHUA through several standard test function experiments and compare it with other classic optimization algorithms such as genetic algorithms, simulated annealing, and particle swarm optimization. Experimental results show that PHUA has good performance in solving multi-dimensional complex optimization problems. Compared with traditional algorithms, the PHUA algorithm converges faster, has stronger global search capabilities, and is better able to escape local optima. Standard benchmark functions such as Sphere, Rastrigin, and Rosenbrock were used in the experiment, and the performance was compared by indicators such as accuracy and convergence speed. Statistical significance tests (such as t-tests) confirmed the robustness and superiority of the results. The PHUA algorithm is particularly suitable for practical applications such as educational resource scheduling and adaptive learning optimization. Although the PHUA algorithm shows excellent performance, it also has limitations such as moderate computational cost and sensitivity to parameter settings.
Implementation of a Synergetic Controller for a 2-DOF Helicopter on an Embedded Platform Using an STM32 Microcontroller Chiem, Nguyen Xuan; Phan, Tran Cong; Thai, Pham Duy; Hai, Bui Xuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13410

Abstract

This study introduces the development of a synergetic control scheme for a two-degree-of-freedom (2-DOF) helicopter, integrated into an embedded system utilizing the STM32 microcontroller. A discrete-time controller is formulated for both pitch and yaw motion, relying on stable manifold design within the framework of synergetic control. Lyapunov-based analysis is used to ensure system stability. The controller is implemented on an STM32F4 device and coded in the C programming language. System performance is assessed through numerical simulations and real-time testing, with results demonstrating strong control precision and feasibility on the physical experimental platform.
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.

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