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Alfian Maarif
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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 15 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): June" : 15 Documents clear
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.

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