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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 378 Documents
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A Hybrid Vision Transformer–Graph Neural Network Framework for High-Resolution Surveillance Scene Understanding Abeer Ahmed Ali; Abdullah Ghanim Jaber; Ali A. Mahmood; Mohammed Jamal Salim
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16148

Abstract

Real-time processing and accuracy of surveillance systems in a real-world environment are very critical to the understanding of the scene. Current deep learning methods, however, have failed to sufficiently learn the global context and relations between objects from high resolution surveillance information. In this study, a hybrid deep learning framework is proposed, which incorporates the merits of Vision Transformers, Graph Neural Networks, and attention-based fusion to tackle these challenges. The novelty of the research is that they design a single architecture combining global features extraction, and relational reasoning to better interpret the surveillance scene. The proposed model has an improved representation of features, which includes complementary mechanisms in a single model. In terms of method, Vision Transformers are used to use global spatial features from surveillance frames. Relationships between the detected objects are described in a structured graph using the Graph Neural Networks. In complex scenes, these features are adaptively integrated together through an attention guided fusion scheme, which improves the classification and understanding of complex scenes. Experimental results on UA-DETRAC dataset show that the proposed framework achieves higher accuracy, precision, recall, F1 score, mAP and AUC (0.962%, 0.955%, 0.958%, 0.956%, 0.960% and 0.97 respectively). Furthermore, the model's performance is 78 fps making it a compatible model for real-time surveillance systems. Analysis of these results shows that the method is robust and improved consistently compared with the baseline methods in all performance measure. Last but not least, the proposed framework is very efficient and accurate, and is able to achieve a good balance between accuracy and computation efficiency in real-time intelligent surveillance. The results prove its potential application for surveillance, traffic monitoring and security critical environments in smart cities.
Fuzzy Logic-Based Distributed and Centralized MPPT Architectures for Photovoltaic Systems: A Comparative Study under Dynamic Irradiance and Partial Shading Conditions Waleed Mahmood Abdullah Al-Darraji; Waladdin Mezher Shaher; Yousif Azzawi Hachim; Abdulbari Talib Naser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16356

Abstract

Maximum Power Point Tracking (MPPT) is very important to enhance the energy harvesting capability of photovoltaic (PV) systems under various environmental conditions. But, under dynamic irradiance and partial shading conditions, conventional MPPT architectures may be plagued by poor tracking performance and energy extraction. This study investigates the use of a fuzzy logic controller (FLC) for MPPT and evaluates its performance in both Centralized Maximum Power Point Tracking (CMPPT) and Distributed Maximum Power Point Tracking (DMPPT) architectures. The key outcome of the present study is the comparison of FLC based CMPPT and DMPPT configurations under dynamic irradiance. MATLAB/Simulink based models are established to evaluate the performance measurement with irradiance profiles of 500-1000 W/m2. The assessment included tracking efficiency, convergence behavior, steady state oscillations and recuperating energy capability under various operating scenarios. The simulation results demonstrate that the proposed FLC exhibits a faster response to changes and better tracking capability compared to the baseline MPPT methods considered in this study. The controller achieved tracking performance up to 97% and reduced stable-state oscillations by above 60%. The DMPPT configuration with fuzzy logic control harvested significantly greater amounts of energy than the respective CMPPT configuration under partial shading conditions. The results show that the use of fuzzy logic control with distributed MPPT architectures can improve the robustness and energy harvesting in irregular irradiance conditions, and therefore, this method can be applied to modern photovoltaic systems.
Hybrid Temporal–Spatial Electroencephalographic Classification Using Principal Component Analysis and Ensemble Learning: Epilepsy, Attention-Deficit/Hyperactivity Disorder, and Schizophrenia Dawood S. Hasan; Qais Al-Gayem; Hilal Al-Libawy
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16803

Abstract

Electroencephalography (EEG) allows noninvasive access to temporal brain activity; however, many EEG classifiers are tailored to a single dataset or disorder and are not easily compared across heterogeneous recording settings. A gap that this study fills is the evaluation of a fixed hybrid temporal–spatial EEG representation pipeline with an identical processing flow on public datasets for epilepsy, attention-deficit/hyperactivity disorder (ADHD), and schizophrenia. Each window passes through a one-dimensional temporal branch and a two-dimensional spatial branch. Their outputs are projected and concatenated into a 256-dimensional fused vector, then standardized and reduced to 64 components by principal component analysis (PCA) fitted only on the training data. The six post-fusion classifiers were tested under dataset-specific validation protocols, with experiment-specific blending applied where applicable, without using held-out test data for scaling, PCA fitting, model selection, threshold adjustment, or blending. Subject-wise ADHD classification achieved around 98.62% accuracy and approximately 98.60% macro-F1. Under 28-fold leave-one-subject-out evaluation, schizophrenia classification yielded an aggregated segment-level accuracy of 96.28% and a fold-averaged macro-F1 score of 73.92%. The selected intra-subject CHB-MIT chb02 experiment attained a seizure-class F1 of 92.31%. The internal four-class experiment was considered exploratory feature-space analysis rather than evidence of dataset-independent clinical diagnosis, as the classes were drawn from disparate datasets and acquisition conditions. The results provide a consistent engineering baseline across the investigated protocols and may inform future EEG decision-support studies, although broader clinical interpretation requires harmonized cohorts, broader multi-subject evaluation, and external validation.
A Load-following Particle Swarm Optimization-based Energy Management Technique for Integrating Electric Vehicles into a Renewable Microgrid System Zaid KH. Sadane; Mustafa Naozad Taifor; Arwa Amer Abdulkareem; Naseer T. Alwan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 4 (2026): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i4.16850

Abstract

The growing incorporation of Electric Vehicles (EVs) and Renewable Energy Sources (RES) into power systems presents new challenges and opportunities for the operation of microgrid (MG). To solve these limitations, this article presents a novel load-following particle swarm optimization (LF-PSO)-based energy management strategy (EMS) for on-grid renewable microgrids with electric vehicle (EV) integration. The proposed EMS optimally coordinates bidirectional power flow between the photovoltaic (PV) system, EV and MG thereby ensuring stable DC-link voltage regulation, improved power quality, efficient battery management, and enhanced overall system energy efficiency. The studied microgrid in this paper is composed of a 21 kW photovoltaic array, a 355 V lithium-ion battery (60 Ah), a 750 V DC bus, a 400 V utility grid, and an electric vehicle load. The assessment of the suggested EMS has been conducted in MATLAB/Simulink across diverse irradiation and load scenarios. The findings demonstrate that the suggested EMS shows improved efficacy, thus guaranteeing the management of the EV and PV system during all atypical circumstances. The obtained results are compared with the results obtained by the classical EMS methods such as proportional-integral (PI) and artificial intelligence (AI) techniques. The proposed LF-PSO strategy reduces the DC-link voltage overshoot from about 30% to less than 5%, which is a reduction of 83.3%, and improves the transient response, power quality, and renewable energy utilization compared with the conventional PI controller.
Reward Based Glider Snake Optimization: An Improved Metaheuristic Algorithm with Reward Mechanism as Decision Making Purba Daru Kusuma; Budhi Irawan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16900

Abstract

There are many new metaheuristic algorithms (MA) introduced in recent years. Most of them employ multiple search strategy. Unfortunately, many of these algorithms accommodate this multiple search strategy by using predefined mechanism which is not adaptive on handling circumstance during optimization process. Moreover, many of these mechanisms are memoryless. Due to this problem, we propose a new decision-making model to handle this multiple search strategy developed based on reward-based mechanism which is adopted from reinforcement learning (RL). Then, this mechanism is implemented into a new existing MA known as glider snake optimization (GSO) so that it becomes variant of it known as reward-based glider snake optimization (RGSO). Two variants are introduced in this work which are RGSO-1 and RGSO-2. RGSO-2 is an extended version of RGSO-1 as an additional search is included. The performance of both variants is investigated using 23 classic functions and three cases of economic load dispatch problems (ELDP). Five existing algorithms including GSO are chosen as benchmarks. The result shows that RGSO-2 is superior to GSO and all other benchmarks in almost all functions and maintains its competitiveness in ELDP. RGSO-2 is better than GSO in 12 functions. Meanwhile, RGSO-1 is comparable to GSO in all functions and ELDP. Moreover, RGSO-1 achieves the optimal solution for two functions while RGSO-2 achieves the optimal solution for five functions.
A Comparative Study of Three Multi-Objective Formulations for NSGA-II-Based Multi-Robot Path Planning Ariadie Chandra Nugraha; Oyas Wahyunggoro; Adha Imam Cahyadi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.16915

Abstract

Existing works utilizing NSGA-II in multi-robot path planning have been primarily concerned with problem formulations where the aggregation of robot objectives, individuality of the objectives defined per each robot, and modeling of the collision avoidance through constraints and optimization objectives differ. The research contribution is systematic comparative analysis of three multi-objective optimization problem formulations within the same NSGA-II framework: (i) an aggregated objective formulation without constraints; (ii) a per-agent objective formulation with constraints; and (iii) a per-agent objective formulation without constraints. Initial population of NSGA-II are initialized using A* algorithm, while the formulations are tested on twelve scenarios comprising two to five robots for three 25 × 25 environments. Ten independent experiments are run for each scenario, and the non-parametric statistical tests are used. Aggregated and per-agent formulations with constraints maintain full feasibility for all scenarios and provide statistically equal results on the total travel time of up to four robots. On the other hand, the per-agent formulation without constraints loses feasibility and demonstrates highly inferior results for three or more robots. Front quality measures (hypervolume and inverted generational distance) calculated in a common objective space prove this and demonstrate the loss of diversity of the latter formulation in scenarios with five robots. The per-agent formulation with constraints is preferable: it matches the aggregated formulation in solution quality while exposing per-robot travel-time trade-offs and a clear Pareto front. This demonstrates the importance of problem formulation in affecting the convergence and termination behavior of NSGA-II.
Enhancement of MPPT Control in PV-Wind Hybrid Systems Using a Hybrid HWOA-GWO Optimization Algorithm Asaad Ali Muhsen; Homam Monem Kadhim; Nibras Hazim Abbas
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.17012

Abstract

Hybrid photovoltaic (PV)-wind systems require maximum power point tracking (MPPT) controllers that remain effective under source intermittency, shared DC-link dynamics, battery constraints, and measurement noise. This research proposes an event-triggered hybrid HWOA-GWO MPPT framework in which a Grey Wolf Optimizer performs population-based global exploration and a whale-inspired spiral operator refines elite candidates, while a filtered adaptive perturb-and-observe tracker maintains the operating point between major regime changes. The research contribution is a converter-aware duty-cycle formulation that embeds MPPT decisions within a coupled PV-wind-battery DC microgrid rather than evaluating the PV source in isolation. MATLAB co-simulation is used to assess tracking efficiency, PV power ripple, DC-link behavior, battery power, state of charge, and settling behavior under four irradiance regimes and measurement noise. Across the four regimes, the mean tracking efficiency increases from 98.177 ± 0.100% for noisy adaptive P&O to 99.386 ± 0.047% for the proposed method. Mean PV power ripple decreases from 13.073 ± 2.249% to 6.523 ± 1.541%, corresponding to an approximately 50.1% reduction, while mean settling times remain comparable. The DC-link response remains bounded and the battery operates within the imposed saturation logic. The results indicate that event-triggered global repositioning combined with low-cost local tracking improves energy capture and ripple suppression without claiming unsupported CPU-time performance.
A Hybrid Ensemble Empirical Mode Decomposition–Temporal Convolutional Network Framework with LightGBM Residual Correction for Day-Ahead Electric Load Forecasting Trung Dung Nguyen; Tuan Anh Nguyen
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 5 (2026): October
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i5.17013

Abstract

Accurate day-ahead load forecasting is essential for generation scheduling, reserve allocation, electricity-market operation, and reliable power-system management. However, half-hourly electricity demand is nonlinear and non-stationary, with short-term fluctuations, daily and weekly periodicity, and slowly varying trends occurring simultaneously. This study proposes a unified hybrid framework that combines ensemble empirical mode decomposition, a temporal convolutional network, and validation-calibrated LightGBM residual correction for direct day-ahead forecasting. The main contribution is the coordinated use of multi-scale signal representation, direct multi-output temporal learning, and controlled residual correction within a single forecasting pipeline. Ensemble empirical mode decomposition separates the original load series into nine intrinsic mode functions and one residue, which are organized as a synchronized ten-channel input. The temporal convolutional network then maps a seven-day historical window of 336 half-hourly observations directly to the subsequent 48 load values, avoiding recursive error propagation. LightGBM is subsequently trained to estimate structured residuals using the base forecast, forecast horizon, calendar variables, historical lags, rolling statistics, and decomposition-derived features. To limit overcorrection, the residual model is trained on an earlier validation subset, while a separate chronological calibration subset selects the correction coefficient, which was 0.30 in the reported experiment. Experiments using Queensland electricity-demand data from 2015 to 2019 yielded an RMSE of 156.92 MW, an MAE of 116.48 MW, a MAPE of 1.8825%, a WAPE of 1.8746%, and an R² of 0.9692 for the proposed framework. Under the stored offline evaluation setting, these error values were lower than those reported for the standalone LightGBM and raw-series temporal convolutional network baselines. Because the baseline pipelines contain closely matched but not completely identical test-origin sets, the comparisons are interpreted descriptively rather than as fully paired statistical evidence. In addition, EEMD is applied offline to the complete series; therefore, the reported results represent a decomposition-assisted benchmark rather than a strictly causal real-time forecasting implementation. The findings suggest that multi-scale representation, temporal convolution, and calibrated residual learning can provide complementary benefits under the considered experimental setting.

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