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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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A Novel Approach to Multi-Task Prediction in IoT Environments Using Dynamic Heterogeneous Graph Neural Networks Chia-Ching Lin; Yih-Chang Chen
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.v8i3.16044

Abstract

The rapid proliferation of Internet of Things (IoT) devices and digital services has created densely interconnected socio-technical networks that challenge conventional risk management. Traditional machine learning models struggle to capture multi-hop relational structure, temporal drift, and severe class imbalance, while static Graph Neural Networks (GNNs) ignore dynamic graph evolution and high-frequency IoT telemetry. This paper proposes an IoT-driven dynamic prediction framework that conceptualizes financial and social-protection infrastructures as assets requiring predictive maintenance. We introduce a multi-task dynamic heterogeneous GNN that jointly estimates credit risk in financial networks and early-warning scores for high-risk social-work cases over evolving graphs linking clients, accounts, devices, households, and practitioners. The architecture combines relation-aware message passing with a Gated Recurrent Unit (GRU) temporal encoder to integrate streaming IoT signals with longitudinal administrative data. We specify an evaluation protocol that compares the framework with logistic regression, gradient boosting, temporal sequence models, and static GCN/GraphSAGE using AUC-ROC, AUC-PR, Brier score, and operational metrics under time-based splits and rolling-window validation; future empirical studies should also employ appropriate significance tests, such as DeLong’s test and bootstrap confidence intervals. This work does not report new experiments on proprietary datasets; instead, it synthesizes recent findings in credit risk, anti-money laundering, IoT-based predictive maintenance, and child-protection analytics to argue that dynamic multi-task graph modeling can better support timely, ethically governed interventions in complex socio-technical systems.
Maximization Very Short-Term Forecasting of Power Photovoltaic System Using Machine Learning Based on Clearness Index Model Unit Three Kartini; L. Endah Cahya Ningrum; M. Nur Adiwana
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.v8i3.16181

Abstract

The hybrid model for very short-term photovoltaic (PV) power forecasting, covering one hour ahead with 20-minute intervals, combines the k-nearest neighbour (k-NN) and multilayer backpropagation neural network (BP-NN) methods. The uniqueness of this model lies in integrating meteorological and the clarity index. the data preprocessing stage, the k-NN method is applied, while the multilayer BP-NN is used for forecasting. The k-NN Multilayer BP-NN algorithm calculates the nearest data points using Euclidean distance, and then processes the training and testing data through the multilayer BP-NN to generate PV power predictions. The simulation dataset was divided into 70% training data and 30% testing data, with a maximum PV power output of 611 W. The error statistical indicators of machine learning using k-NN-BP-NN model RMSE 27.44 W and MSE 1.5 W. These superior results are attributed to more stable weather patterns and consistent solar radiation. The simulation validity test demonstrated that the k-NN Multilayer BP-NN algorithm achieved better accuracy compared to the k-NN decomposition method. In addition, the model offers high computational efficiency and short inference time, making it highly suitable for real-time PV power forecasting systems.
Adaptive Hybrid Fuzzy–Neural PID Control of Speed Regulation and Torque Ripple Reduction of BLDC Motors Riyadh Kamil Chillab; Hasan Ali Hasan
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.v8i3.16182

Abstract

This paper focuses on the control of Brushless Direct Current (BLDC) motors utilizing an enhanced fuzzy control and Neural Network (NN) located Proportional–Integral–Derivative (PID) control arrangement that acts as real-time mistake adaptation and adjustment to regulate engine speed. The intelligent optimization algorithm is also used to embellish the action of the fuzzy PID controller. BLDC motors are settled in production, conveyance, and meet extreme-precision requests next to their plain creation, reliable movement, and superior speed control competence. Improving the control veracity of BLDC motors is a main research issue, and some improvements have been made in the current age. Conventional (PID) control algorithms have a simple form and expansive relevance and are usually used for BLDC engine speed control. However, these algorithms do not efficiently detect differences in load conditions. To address this restraint, an FLC and interconnected system-based PID control means is executed to regulate and correct control errors in real time. In order to improve BLDC motor dynamic performance across a range of load circumstances, the suggested hybrid NN-PID controller will integrate FLC adaptation with NN learning. Simulation results show that the projected means correct the speed error at 0.2 s from 22.3 to −0.102, from 22.5 to −0.305, and from 38.5 to −13.474, distinguished by accompanying (NN), FLC rationale, and conventional PID controllers, individually. In addition, the projected approach reduces torque ripple by 64.13%, 68.3%, and 74.56% distinguished with NN, fuzzy sense, and PID controllers. The substitution results represent the usefulness of the pertinent whole form in threatening speed, error, and torque ripple.
Sliding Mode based Circle Search Algorithm for Inventory Control with Extended State Observer Neamah D. Farhan; Raya K. Naji; Muntaha K. Musa; Huda A. Kanber; Wafaa H. Abdul Hadi; Aws M. Abdullah; Hussien Dulaimi; Huthaifa Al-Khazraji
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.v8i3.16220

Abstract

The efficient utilization of inventory systems has become increasingly important in modern industrial practice due to its substantial contribution to cost reduction, resource optimization, and overall operational efficiency. Sliding mode control (SMC) is presented in this study for management and optimization of the inventory systems. First, the differential equations of inventory system are developed. Then, the SMC is employed for the improvement of the performance of the inventory system under time varying demands, by minimizing tracking error and improving reference following. Moreover, an extended state observer (ESO) is introduced to compensate for the lack of direct access to the system state variables by estimating the unavailable states while simultaneously providing demand estimation. Furthermore, circle search algorithm (CSA) is used for optimizing the SMC and the ESO because of its outstanding global optimization potential and to provide a good balance between exploitation of the obtained solutions and exploration of new ones during the search process by means of the integral of absolute error (IAE). The efficacy of the ESO to estimate the states of the system and the profile of the unknown demand has been validated by computer simulation in the MATLAB. Additionally, The proposed ESO-SMC is examined with the proportional, integral, derivative (PID) controller of the stochastic demand. This test shows the ESO-SMC to be superior in their performance enhancement, especially the reduction of inventory costs.
An Enhanced 3DOF PID Control Scheme for Boost Converters with Improved Transient and Steady-State Performance Abrar Abdul Hameed Rasheed; Marwan Kanaan Ismael; Ali Sachit Kaittan
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.v8i3.16253

Abstract

This article discusses a novel Three-Degree-of-Freedom (3DOF) PID control design for DC-DC boost converters aimed at improving transient and steady-state performance. Boost converters are essential in power electronic devices but present challenges due to their nonlinear characteristics and right-half-plane zero issues that hinder traditional control methods. Basic PID controllers often exhibit faults like overshoot and poor disturbance rejection. The proposed 3DOF PID controller addresses these issues by incorporating set-point weighting and separating feedforward from feedback control, allowing for independent tuning of reference tracking and disturbance rejection. A systematic design approach is employed to optimize controller parameters for various operating conditions. The controller was implemented in a MATLAB/Simulink environment and tested against a detailed boost converter model. Simulation results show that the 3DOF PID controller significantly reduces rise time from 0.45 s to 0.18 s, settling time from 0.8 s to 0.3 s, and overshoot to under 2% compared to standard PID controllers, which typically show 10-12% overshoot. Additionally, under load disturbance, the voltage dip is reduced from 3 V to 1.2 V, with recovery time improved from 0.5 s to 0.2 s. Overall, the findings confirm that the 3DOF PID controller enhances transient response, disturbance rejection, and stability, making it a promising solution for high-performance power electronic applications. The acquired data confirm that the suggested 3DOF PID controller improves the boost converter's transient and steady state performance while preserving its stability in dynamic situations.
A Comparative Study of Vision Transformers and Convolutional Neural Networks for Lung Nodule Malignancy Classification in CT Imaging Aya Ahmed Hashim; Emtiaz Abbas Naji; Estqlal Hammad Dhahi; Shahad Dakhil Khalaf; Zahraa Shams Alden; Ayad Hameed Mousa
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.v8i4.14422

Abstract

Accurate and timely malignancy categorization of pulmonary nodules in computed tomography (CT) images is critical for Health Information Technology, directly impacting clinical decision support systems and patient prognosis in lung cancer management and patient prognosis in lung cancer management. Although Convolutional Neural Networks (CNNs) are the standard, their local inductive bias can make them weak with regard to the modelling of the long-range, global contextual dependencies of medical images. While we recognize the natural restriction of evaluating 2D axial slices instead of full 3D volumetric data. This paper evaluates the effectiveness of a pre-trained self-supervised Vision Transformer (ViT) model to classify binary lung nodules, and leveraging the model's global self-attention mechanism to extract complex morphological features. Using a rigorously curated cohort of 2186 pulmonary nodule instances from the public LIDC-IDRI dataset, we preprocessed data via windowing, normalization, and resizing to 224×224 pixels. A ViT-Base model, pre-trained on ImageNet-21k, was fine-tuned and evaluated against a strong CNN baseline (DenseNet-121) using five-fold cross-validation. The ViT model achieved a superior F1-score of 0.891 (±0.018) and a mean AUC-ROC of 0.945 (±0.012) on the held-out test set. The results demonstrate that the Vision Transformer architecture presents a highly effective framework for this diagnostic task within HIT, surpassing traditional CNN-based approaches. Future work will focus on integrating 3D spatial information across multiple CT slices to further enhance model performance and clinical utility.
Integrating Tongue and Dental Biometrics Using Improved PSO for Enhanced Forensic Evidence Tameem Hadi Fadhil
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.v8i4.15039

Abstract

Reliable human identification is a challenging area in forensic science especially where conventional biometrics are not available. This is addressed in this study by coming up with a new multimodal system. The study contribution is the Adaptive Convergence-Triggered Mutation Particle Swarm Optimization (ACTM-PSO) algorithm of optimal fusion regarding tongue and dental biometrics. The combination leverages durability regarding dental structure as well as the specialized texture of tongue prints, which in most cases preserved in post-mortem cases. The technique extracts the texture and morphological features of tongue and dental images. The proposed ACTM-PSO, which has non-linear adaptive inertia weight and stagnation-triggered mutation, optimizes the weighting of features for fusion before classification using an SVM. Experimentally, the system attains accuracy of 97.3% and low Equal Error Rate (EER) of 2.1% in comparison to traditional PSO (88.3% accuracy) and unimodal systems. It has an accuracy of more than 95% in rotations of the image (0°, 90°, 180°, 270°), which is forensically practical. The False Match Rate (FMR) and the False Non-Match Rate (FNMR) are 2.0% and 3.3%. This paper provides a strong optimization based backbone that goes ahead to develop multimodal biometric integration to be used in a forensic application to increase the reliability of evidence in legal contexts.
Meta-Heuristic Hyperparameter Optimization for Q-Learning: A Snake Optimizer Approach to ‎Computation Offloading in the Industrial Internet of Things Hayder Salman Dawood; Ismail Fauzi Bin Isnin; Muhalim bin Mohamed Amin; Ahmad Najmi bin Amerhaider Nuar
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.v8i4.16274

Abstract

The proliferation of computationally intensive Industrial Internet of Things (IIoT) applications requires offloading policies that reduce latency and energy consumption under constrained device, edge, and cloud resources. Although reinforcement learning (RL) is suitable for sequential offloading decisions, its performance is highly sensitive to hyperparameters that are often fixed or manually tuned. The research contribution is an automated QLSO framework that uses the Snake Optimizer (SO) to tune the learning rate, discount factor, and exploration-decay rate of tabular Q-learning (QL) in a three-tier IIoT-edge-cloud architecture. Unlike generic external Hyperparameter Optimization (HPO)‎ methods, QLSO embeds SO as a derivative-free, population-based tuning layer within the QL offloading workflow, where candidate hyperparameters are evaluated based on latency-energy-aware performance under three-tier IIoT resource constraints. The problem is formulated as a Markov decision process with discrete local, edge, and cloud execution actions, and candidate hyperparameters are evaluated through episodic training followed by greedy validation. Across five independent random seeds, QLSO converged about 1.5× faster in the complex scenario and achieved lower latency and energy than the QL and SARSA baselines, while remaining close to the strongest raw-metric baseline, DQN (0.0493 s and 1.72×10⁻² J for QLSO versus 0.0488 s and 1.65×10⁻² J for DQN). However, QLSO achieved the highest latency-optimal decision rate (49.0%, +3.3 percentage points over DQN), and the ablation study showed a 9.1% fitness improvement over fixed-parameter QL. The framework remains simulation-based and single-agent, requiring validation with partial offloading, dynamic network models, and realistic platforms such as NS-3, CloudSim, EdgeCloudSim, or physical IIoT testbeds.
Static-Slope Dynamics and Uncertainty of a Compact-Excavator Manipulator for Near-Ground Sensing Pham Chi Thanh; Tran Ngoc Binh; Nguyen Xuan Chiem
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.v8i4.16278

Abstract

Near-ground unexploded ordnance (UXO) sensing requires a mobile carrier that maintains a controlled detector air gap over uneven terrain. This paper develops a static-slope rigid-body baseline for a Bobcat E20-class mini excavator carrying a 15 kg VMF4-class payload on a self-leveling gimbal. The research contribution is a body-frame, parked-base formulation that separates arm-level Cartesian positioning from payload-level attitude stabilization and identifies which rigid-body terms change on a static slope. The four-DOF attachment is reduced to a planar three-DOF boom-stick-tool subsystem for line sweeping. A kinetic-energy argument and potential-energy differentiation show that, with parked chassis and body-fixed coordinates, the inertia and Coriolis/centrifugal terms retain their level-ground form, while only the gravity torque is recomputed from the rotated gravity vector and center-of-mass Jacobians. Evaluation combines static torque analysis, Monte Carlo propagation, computed-torque tracking, and runtime timing. At 10° pitch, the 95th-percentile end-effector uncertainty is 2.63-3.65 cm; at 15° pitch, the tested gravity-torque change reaches 322.66 Nm. In the Python reproducibility environment, gravity-only update takes 24.0 microseconds per sample, compared with 38.9 microseconds for full rigid-body term recomputation. The 5.7°-6.3° trigger is a heuristic gravity-bias indicator, not a stability or clearance guarantee. Results support lightweight slope-aware gravity recomputation, while hydraulic dynamics, friction, soil interaction, moving-base effects, embedded timing, and hardware validation remain future work.
Cross-Configured Wideband Multiple-Input Multiple-Output Antenna with Defected Ground Structure and Parasitic Directors for 2.7–12 GHz Ultra-Wideband and Sub-6 GHz Wireless Applications Rami Abduimawjood Mohammed; Uttam Laxman Bombale
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.v8i4.16540

Abstract

Modern Ultra-Wideband (UWB) and sub-6 GHz systems require compact antennas with wide bandwidth, enhanced gain, low coupling, and reliable diversity. This study develops a simulation-based wideband MIMO antenna that addresses the bandwidth and isolation limitations of conventional planar microstrip antennas. The research contribution is a progressive antenna family based on a microstrip-fed planar slot radiator using a partial defected ground structure, parasitic directors, and a lens-shaped substrate. The design is modeled in Ansys HFSS through four stages: an optimized single element, a 1×2 MIMO antenna, a cross-configured 4-port MIMO antenna, and a 1×4 MIMO antenna. The defected ground structure improves impedance matching, whereas the directors and lens-shaped substrate enhance forward radiation. The optimized single antenna achieves a simulated -10 dB impedance bandwidth of 2.72–12.0 GHz and peak gains of 5.54, 6.79, and 6.21 dB at 6.7, 8.7, and 10.7 GHz, respectively. The 1×2 MIMO antenna preserves a 2.7–11.8 GHz bandwidth and achieves gains of 9.25, 9.94, and 8.88 dB, with a worst-case ECC of 0.0038 and a minimum diversity gain of 9.9999 dB. The cross-configured 4-port MIMO antenna provides the highest gains of 9.31, 9.99, and 9.89 dB, confirming the benefit of the orthogonal arrangement. The 1×4 MIMO antenna shows lower gains of 1.253, 1.254, and 1.261 dB, but provides strong diversity, with adjacent- and non-adjacent-port ECC values below 0.0021 and 0.0010, respectively. The results confirm that the proposed antenna family provides a practical trade-off between bandwidth, gain, isolation, and diversity for ultra-wideband, sub-6 GHz, IoT, sensing, and high-data-rate wireless applications.

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