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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 351 Documents
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Improving Mobile Robot Navigation Using Deep Q-Learning with Diagonal Motion under Dynamic Obstacle Environments Karam A Al-Zubaidi; Ahmed M Alkamachi; Bahaa Ansaf
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15637

Abstract

Navigation of the mobile robot in dynamic environments is a significant challenge for researchers due to the uncertainty and rapid changes in obstacle movement. This study proposes a framework for navigating a mobile robot using a deep Q-learning (DQL) algorithm in environments containing both static and dynamic obstacles. The research contribution lies in integrating diagonal motion to enhance manoeuvrability and improve decision-making under dynamic conditions. This paper compares the model with the basic motions model (front, back, right, left). The comparison is conducted across four environments of varying difficulty in terms of the density of obstacles. The model is trained by 3000 episodes using a Deep Q-Network (DQN) with two fully connected hidden layers consisting of 128 and 64 neurons, respectively, employing a greedy policy and utilizes a LiDAR simulator for spatial perception. Both models achieved a 100% success rate in reaching the target without collision in environments A and B, and 90% in environments C and D. However, the proposed approach succeeded in reducing the average number of steps required to reach the goal in all four environments, shortening the path by 10% to 20%. This reduces the time and energy required to reach the goal, which is a significant and crucial advantage in real-world environments. Even when tested at obstacle speeds up to six times the robot's speed, it demonstrated superiority compared to the other model. The model showed very good performance even with noise on the LiDAR reading. In short, the proposed model offers a robust and scalable approach to mobile robot navigation in real-world environments.
Digitizing Waste Management Using the Internet of Things: Research Opportunities Windi Auliana; Q. Qurtubi; H. Haswika; Kongkidakhon Worasan; Mohammad A. Shbool
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15696

Abstract

This study proposes an integrative multi-layered framework to address fragmentation in industrial waste management. The increasing volume of industrial waste creates an urgent need for a more precise, adaptive, and sustainable control system, as current practices often lack sufficient integration to ensure full environmental accountability. A critical gap exists in the lack of integration between real-time technical data and strategic governance, which hinders "intelligent compliance" in industrial settings. This research aims to identify trends, thematic scope, and research opportunities in IoT-based production waste control. The specific contribution of this study is the proposal of an integrative multi-layered framework that synchronizes monitoring, intelligent analytics, and blockchain-based accountability. The method was a PRISMA-based systematic review, search queries including 'IoT', 'Industrial Waste', and 'Blockchain' were applied to the Scopus database. 37 high-impact articles were selected based on three criteria: (1) industrial waste focus, (2) integration of Industry 4.0 pillars (AI, Blockchain, or 5G), and (3) publication within 2020–2025. Focusing on current system maturity over historical protocol evolution, this period reflects the state-of-the-art technological convergence. A rigorous Scopus screening narrowed 147 publications to 37 articles, enabling targeted qualitative synthesis. The results categorize IoT roles into thematic clusters: monitoring, process optimization, and circular economy integration. While promising, challenges such as data interoperability and security costs remain significant. This framework provides a blueprint for automated compliance. Future research should validate this model through cross-industry case studies. Study limitations include the reliance on a single database and the rapidly evolving nature of IoT technologies.
Predefined-Time Super-Twisting Disturbance-Observer-Based Speed Control for PI-Based FOC Induction Motor Drives Quang Huy Le; Ngoc Thuy Pham
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15761

Abstract

This paper proposes a predefined-time super-twisting disturbance-observer-based speed control strategy integrated into a conventional proportional–integral (PI)-based Field-Oriented Control (FOC) framework for induction motor drives. Although PI regulators are widely adopted in industrial FOC systems due to their simplicity, low computational burden, and practical reliability, the mechanical speed loop remains vulnerable to load torque variations and parameter uncertainties, resulting in degraded transient performance and limited disturbance rejection capability. To address this issue, a predefined-time super-twisting disturbance observer is incorporated exclusively into the outer speed loop, while standard PI controllers are preserved for current and flux regulation, thereby maintaining structural simplicity and industrial compatibility. Specifically, a nonlinear predefined-time sliding manifold combined with time-varying adaptive gains is constructed to explicitly shape the convergence behavior and guarantee that the speed tracking error converges within a user-defined time independent of initial conditions and without requiring prior knowledge of disturbance bounds. Rigorous Lyapunov analysis establishes predefined-time stability of the disturbance estimation and overall closed-loop system. Simulation results under speed reversals, step load variations, and stochastic disturbances demonstrate that the proposed method achieves faster convergence, reduced overshoot, and enhanced robustness compared with conventional PI-based FOC, while preserving low implementation complexity and practical feasibility for industrial drive applications.
Review on User Interaction for Robotic Arm in Digital Twin Aiman Hakim Azahari; Mohd Khalid Mokhtar; Nazreen Abdullasim; Mohd Hafiz Bin Zakaria; Asniyani Nur Haidar Binti Abdullah; Shafina Binti Abd Karim Ishigaki; Ikmal Faiq Albakri Mustafa Albakri; Muhamad Najib Zamri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15851

Abstract

The integration of human interaction techniques in digital twin (DT) systems has become increasingly important in manufacturing, industrial automation, and remote operations, particularly for robotic arm control. However, existing approaches joystick control, gesture-based input, and virtual reality (VR) are often disconnected across modalities, limiting effectiveness in real-time environments. The research contribution is a systematic literature review (SLR) that critically analyzes and synthesizes interaction techniques to identify performance trends, evaluation gaps, and design challenges in virtual robotic arm control within digital twin frameworks. The review covers studies published between 2020 and 2025, selected to reflect the rapid emergence of immersive technologies in real-time digital twin systems. Following PRISMA 2020 guidelines, 180 records were identified from IEEE Xplore, Scopus, and Web of Science, from which 77 peer-reviewed studies were selected. Interaction techniques were evaluated using task completion time, positional accuracy, NASA Task Load Index (NASA-TLX), and System Usability Scale (SUS). The findings reveal that VR-based techniques dominate due to their intuitiveness and immersive experience in human-in-the-loop control. However, evaluation remains inconsistent across studies, with significant variation in metrics and experimental setups. Latency and synchronization were identified as critical challenges in real-time control, where delays degrade precision and responsiveness. Traditional methods such as joysticks offer stability but lack the natural interaction of immersive techniques. These findings underscore the need for standardized evaluation frameworks and improved synchronization strategies, offering practical guidance for designing robust, human-centered digital twin interaction systems for robotic arms.
Utilizing Two Maximum Power Point Tracking Techniques with an Integrated DC-DC Boost Converter for Controlling a Grid-Connected Photovoltaic System Haitham Ibrahim Elbahat; Ibram Y. Fawzy; Ahmed A. Zaki Diab; Hamdy Sultan; Ahmed Kassem; Alfian Maarif; Mahmoud A. Mossa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15933

Abstract

This paper focuses on the comparison of maximum power point tracking ‎‎(MPPT) techniques for photovoltaic (PV) system that is interfaced with the ‎utility ‎grid via a three-phase voltage source inverter (VSI). Two algorithms for ‎MPPT ‎are presented: the Perturb and Observe (P&O) technique and the ‎Incremental Conductance (INC) algorithm, ‎which employ a DC-DC boost ‎converter.‎ The algorithms are designed to optimize the capture of power produced by the PV system by measuring the PV output power and adjust the converter’s duty cycle. The VSI in the system handles the conversion from DC to AC. It employs both an internal control loop and an external control loop to maintain the stability of the DC-link voltage and to ensure synchronization with the grid. The grid synchronization of system involves the use of Phase-Locked Loops (PLL) to achieve high accuracy in dynamic conditions. The MPPT algorithms are implemented purely in a simulated environment using the Matlab/Simulink package to illustrate the advantages of the presented MPPT methods in comparison to operating without MPPT under different meteorological conditions. The PV array simulation generally employs monocrystalline modules. The electrical parameters of the system comprise the maximum power point voltage (Vmpp), maximum power point current (Impp), open-circuit voltage (Voc), and short-circuit current (Isc). The system initiates operations under standard test conditions (STC) of 25°C and 1000 W/m² during the simulation, followed by variations in irradiance (G) and temperature (T) over time. The findings‎ indicate that the P&O technique effectively tracks the maximum power point (MPP) and facilitates the extraction of further power throughout fluctuations under various meteorological conditions. Furthermore, the INC algorithm is determined to be more effective for achieving MPPT in relation to both the P&O method and the absence of MPPT under dynamic as well as steady-state conditions‎. The INC algorithm is shown to increase the PV output power at STC by 5.24% without utilizing ‎MPPT, whereas the P&O algorithm achieves an enhancement of 3.24%.‎ The results also reveal that the INC technique exhibits the highest performance, achieving approximately 99.72% efficiency, whereas P&O reaches nearly 97.62% efficiency at STC.
Motorcycle Parking Availability Monitoring Using YOLOv5 and Mobile-Based Systems R. Endro Wibisono; Anita Susanti; Kusuma Refa Haratama; Widi Aribowo; Karin Nur Fitria Ariyanti; Diego Oliva; Hisham A. Shehadeh; Abubakar Umar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16087

Abstract

The increasing number of motorcycles in developing countries has intensified parking management challenges, particularly in high-density environments with irregular vehicle arrangements. This study proposes a motorcycle parking availability detection system using the YOLOv5 object detection algorithm to address limitations of conventional parking methods. The research contribution is the development of a context-aware detection framework using a locally collected dataset and the evaluation of its performance under real-world parking conditions.The dataset consists of 1,200 images collected from campus parking areas and is divided into training, validation, and testing sets. The images were annotated into occupied and vacant classes and trained using YOLOv5 with 100 epochs. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP@0.5) on a held-out test set.The results show that the model achieves an F1-score of 0.57 and mAP@0.5 of 0.566, indicating moderate detection performance in dense and occluded environments. Although a precision of 1.00 is obtained at a confidence threshold of 0.978, this condition significantly reduces recall, highlighting a trade-off between detection accuracy and coverage. The confusion matrix and recall–confidence analysis reveal that errors are primarily caused by occlusion, shadow effects, and background interference. Compared to previous studies focusing on car parking detection, this system demonstrates comparable performance while addressing the unique complexity of motorcycle parking. However, the relatively small dataset size and environmental variability limit generalization.In conclusion, the proposed system provides a feasible initial approach for motorcycle parking detection, but further improvements in dataset diversity, annotation quality, and model robustness are required to achieve reliable large-scale deployment.
Review-Simulation Model of Power Electronic Converters by Using MATLAB Salam Waley Shneen; Suaad Makki Jiaad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.14047

Abstract

Power electronics is known as one of the most important branches of electrical science or electrical engineering, focusing on the design of electronic circuits for control and conversion applications of electrical quantities between the input and output of electronic converters. Power electronics is linked to control (control systems) on the one hand and conversion (electrical energy conversion) on the other. Semiconductors (diodes, transistors, and thyristors) are used in power electronics construction. Industrial applications are one of the most important uses of power electronics. The use of power electronics in many fields provides improved energy use with a high level of efficiency, such as in renewable energies (such as solar panels and wind turbines), as well as electric vehicles and related industrial applications. Converters are named according to their function. Rectifiers convert alternating current (AC) to direct current (DC) in either single-phase or three-phase. Converters are constructed using semiconductors, including diodes, transistors, and thyristors. The number of electronic switches varies depending on the type of converter and is determined by the type of power source and the type of load connected to the converter's output. Another type of converter, called an inverter, works in the opposite direction to a rectifier, converting DC to alternating current in either single-phase or three-phase. To further understand converters, this review presents a literature review of power converters according to research contributions. The first research contribution includes an overview of the power electronics overview. The second research contribution identifies converter types, and the third identifies their applications. To further enhance knowledge, we use theoretical mathematical representations of these types of power converters and select simulation models using MATLAB to analyze their behavior through simulation results. Based on the results, appropriate conclusions can be drawn and contributions can be identified for future work.
A Novel Slang and Formal Text Classification with Data Exploration and Optimized Deep Learning Models Hoger K. Omar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.14373

Abstract

Automated text classification involves applying artificial intelligence algorithms to classify text documents into predefined categories. Hence, developing a high-accuracy text categorization model is a significant task, especially in unstructured narratives such as research papers, medical documents, and news articles. This study examines the application of an artificial neural network (ANN) algorithm for categorizing formal and slang English language with the capabilities of popular deep learning frameworks such as TensorFlow and Keras. First of all, the dataset's features were examined through exploratory data analysis (EDA) methods to enhance understanding. Furthermore, the study emphasizes the use of several preprocessing techniques to address the challenge presented by the informal writing style. In addition, adding a list of common English abbreviations greatly improved the accuracy and effectiveness of classifying text. Lastly, the work involves using multiple hyperparameter optimization approaches for further enhancement. The proposed techniques effectively mitigated the impact of heterogeneous and noisy data in both formal and informal language by achieving an improvement of approximately 10% in overall classification accuracy. Additionally, the study contributes to an advancement in the field of text mining and offers practical guidance for optimizing deep learning models in the domain of English text categorization.
Intelligent Control of High Gain Improved Zeta Converter for Hybrid Renewable Energy Management Siddheswar Kar; Manish Jain; Murali Matcha
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.14545

Abstract

The appropriate way to diminish greenhouse gas emissions is the usage of Renewable Energy Sources (RES). Nevertheless, the sporadic nature of these resources has led to random characteristics in the generation and load balancing the microgrid systems. This work proposes an advanced hybrid energy management system that incorporates PV and wind systems with battery to assure continuous supply of power. The system integrates an innovative High Gain Improved Zeta Converter (HGIZC) for effective PV energy conversion, allowing high step-up voltage gain with reduced losses. The HGIZC converter uses voltage multiplier cell that enables it to attain maximum voltage efficiently with decreased component stress. A Chaotic Bacterial Foraging Optimized Proportional Integral (CBFO-PI) controller is developed to dynamically manage the converter output and sustain a stable DC bus voltage. The chaos theory is utilized in CBFO to alter the PI controller’s parameters to sustain a steady voltage. The Wind Energy Conversion System (WECS) utilizes a Doubly Fed Induction Generator (DFIG) coupled with a rectifier to extract maximum energy from wind resources. The benefit of battery is store the power produced by the hybrid energy systems and the bi-directional converter performing charging/discharging function of battery. This research is implemented in MATLAB tool, exposes the converter efficacy of 98.03% with reduced THD 1.03 %. The developed research optimizes power flow among PV, wind and battery systems, assuring stability and allowing reliable hybrid renewable energy integration, thereby efficiently alleviating the variability caused by the sporadic nature of renewable sources.
A Hybrid Weighted Soft-Voting Ensemble with Integrated Preprocessing for Breast Cancer Classification on the WDBC Dataset Mohammad Khalaf Rahim Al-Juaifari; Israa Mohammed Rahi Alabudy; Ammar Rasoul Mohammad Alsaachi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.14587

Abstract

Breast cancer continues to be one of the major causes of deaths due to cancer amongst women around the globe, requiring an effective method of diagnosis. In this research, a machine learning model pipeline that uses a hybrid weighted soft-voting based ensemble method on breast tumor classification for binary data is presented using the Wisconsin Diagnostic Breast Cancer Dataset (WDBC). Data preprocessing includes balancing the classes using Synthetic Minority Over-sampling Technique (SMOTE), removing the highly correlated attributes, and reducing dimensionality through Principal Component Analysis (PCA). Using stratified 10-fold cross-validation, an ensemble yielded 99.3% accuracy and 100% recall for the malignant class on PCA-transformed data, which was better than any individual classifier. Logistic Regression had good performance too, with 98.83% accuracy and 99.81% ROC AUC, which shows that our data can be nearly linearly separable. The feature importance analysis showed that “worst concave points” and “mean radius” were the most important features, and this makes sense from a medical perspective. Overall, this work presents an effective methodology for diagnosing breast cancer.

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