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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 326 Documents
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Design and Optimization of Circularly Polarized Dual Band Patch Antenna Using Whale Optimization Algorithm for Wireless Communications Singh, N. Nirmal; Vanitha, K. Komathy; Sumi, M. S.; J, Jarin Joe Rini; Geetha, T.; Ramakrishnan, Sundar
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.v8i2.14340

Abstract

In the realm of wireless communication systems, the demand for high-performance antennas proficient of accommodating numerous frequency bands whereas sustaining circular polarization has seen substantial growth. This work presents a unique approach for addressing this need. The antenna design begins with the incorporation of a patch structure, which, when properly configured, operates at 2.4GHZ and 5 GHz that corresponds to Wi-Fi bonds while simultaneously generating circular polarization. To optimize the antenna's performance, the Whale Optimization Algorithm (WOA) is employed. The WOA is a metaheuristic optimization approach enthused by the foraging behavior of humpback whales' feeding habits, known for its capability to proficiently explore complex solution spaces and find near-optimal configurations. This research emphasizes the importance of circular polarization in various wireless communication applications, comprising RFID systems, satellite communication, and GNSS receivers, among others. The utilization of dual bands ensures flexibility in accommodating multiple wireless standards within a single device. By leveraging the WOA, the antenna's parameters, such as dimensions and feeding techniques, are fine-tuned to maximize its circular polarization and impedance matching capabilities. This study's findings show how effective the suggested strategy is, showcasing a gain and return loss of 2.6353 dBi and -4.58 dB with radiation efficiency is 79.05 % at 2.4 GHZ and 74.83 % at 5 GHZ. Performance metrics, including gain, axial ratio, and impedance bandwidth, are rigorously evaluated, highlighting the antenna's suitability for contemporary wireless systems via wireless methods. Besides antenna design, this research offers plenty of benefits, as the integration of the WOA holds promise for enhancing the performance of other complex engineering systems.
Exploring RRT and BiRRT Algorithms: A Review and Simulation-Based Comparison for Fixed-Wing UAV Path Planning Pratama, Gilang Nugraha Putu; Dhewa, Oktaf Agni; Jati, Mentari Putri; Hidayatulloh, Indra; Gunawan, Teddy Surya; Gunawan, Syaiful Ardy
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.v8i2.14511

Abstract

Path planning plays a vital role in ensuring the safe and efficient navigation of fixed-wing unmanned aerial vehicles (UAVs), particularly in cluttered and complex environments. The increasing demand for autonomous UAV operations highlights the need for reliable algorithms capable of generating optimal and collision-free trajectories. This study addresses the challenge by reviewing recent uses of the Rapidly-exploring Random Tree (RRT) algorithm in various robotic platforms and navigation tasks. The research contribution of this paper is a comparative analysis of RRT and BiRRT for fixed-wing UAV path planning, quantifying trade-offs between path length, computation time, and obstacle clearance using a real-world 2D urban map. This addresses a gap in the literature, as few studies have directly compared these algorithms specifically for fixed-wing UAV surveillance missions. The methods involve implementing both RRT and BiRRT in a simulated environment where each algorithm is evaluated over 100 runs to measure performance metrics such as path length, computation time, and obstacle clearance. A realistic urban map is used to test the algorithms under consistent starting and goal positions. The results show that both RRT and BiRRT achieve a 100% success rate in finding collision-free paths. BiRRT consistently generates shorter paths and requires less computation time, making it more suitable for time-sensitive missions. However, RRT produces safer trajectories with greater average clearance from obstacles, which is advantageous in environments with high collision risk. The findings demonstrate a clear trade-off between safety and efficiency. In conclusion, BiRRT is recommended for missions where speed and efficiency are prioritized, while RRT is better suited for operations emphasizing safety and obstacle avoidance.
Trends and Gaps in Transformer-Based EEG Modeling: A Review of Recent Developments Pamungkas, Yuri; Karim, Abdul; Aung, Myo Min; Uda, Muhammad Nur Afnan; Hashim, Uda
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.v8i2.14933

Abstract

In recent years, Transformer-based deep learning architectures have emerged as a powerful paradigm for modeling EEG signals, offering superior capability in capturing spatial–temporal dependencies compared to traditional convolutional or recurrent networks. However, the diversity of model designs, limited dataset generalization, and lack of standardization have created challenges in evaluating their true potential for real-world applications. This review addresses these issues by systematically examining the evolution, performance, and methodological trends of Transformer-based EEG models published between 2022 and 2024, highlighting both achievements and research gaps. The main contribution of this study is to provide a comprehensive mapping and critical analysis of Transformer architectures applied to EEG classification, feature extraction, and signal decoding tasks. Using the Scopus database, a structured search was conducted following specific inclusion criteria (English, peer-reviewed, open-access journal papers from 2022–2024) and a well-defined query combining EEG and Transformer-related keywords. Data from 63 eligible studies were extracted and categorized according to authorship, dataset, architecture type, EEG application, and evaluation metrics. Results show that hybrid Transformer models dominate recent research, achieving accuracies above 90% in tasks such as motor imagery, emotion recognition, seizure detection, and sleep staging. Pure Transformers like ViT and BERT-like models also demonstrate competitive performance but face scalability and interpretability challenges. In conclusion, Transformer-based EEG modeling is advancing rapidly, yet future efforts must focus on model efficiency, explainability, and benchmark standardization to enable broader clinical and real-world adoption.
Fuzzy Logic-Based Classification of Crescent Moon Images Using Contrast and Thickness Pramudya, Yudhiakto; Firdausy, Kartika; Jufriansah, Adi; Okimustava, Okimustava; Khoirunnisa, Itsnaini Irvina; Murti, Bayu Krisna; Hidayah, Rihmah Alifah; Murinto, Murinto; Maulidan, Muhammad
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.v8i2.14964

Abstract

Accurate determination of the crescent moon (hilal) is crucial for establishing the start of lunar months in the Islamic calendar; however, observations are frequently hindered by daylight conditions, atmospheric disturbances, and subjective visual interpretation. This research proposes a fuzzy logic-based classification system to evaluate crescent moon images using contrast and arc thickness as input parameters, providing a transparent, rule-based alternative to black-box machine learning models for hilal visibility assessment. Images were collected on four distinct observation dates (May 28, 2025, August 5, 2024, September 16, 2023, and May 9, 2021) under varying atmospheric conditions and crescent appearances. Each image underwent pre-processing to extract quantitative measures of arc contrast and thickness, which were subsequently fuzzified using triangular and trapezoidal membership functions. A fuzzy inference system employing expert-defined rules was then used to compute a visibility score for each observation. The resulting visibility scores of 0.4691, 0.4604, 0.4689, and 0.4154, respectively, placed all four observations within the “partially visible” category. These findings demonstrate the system's capability to manage observational ambiguity in daylight conditions, showing potential for reliable classification while still requiring validation on larger datasets and clear non-visibility cases, and offering a transparent and interpretable framework to support more consistent and standardized hilal classification for calendrical purposes.
Adaptive Feature Selection using Fisher-Based Supervised Hill Climbing for Dysgraphia Handwriting Classification Kirana, Kartika Candra; Handayani, Anik Nur; Eva, Nur; Wibawa, Aji Prasetya; Hidayat, Wahyu Nur; Arai, Kohei
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.v8i2.14983

Abstract

Dysgraphia features selection remains a challenge. Fisher’s criterion excels at highlighting the discriminative features of dysgraphia but lacks guidance for choosing the optimal number of features. Whereas Hill Climbing shows robust feature selection but often gets trapped in local optima. This study aims to avoid the Hill Climbing trap in local optima when selecting the best dysgraphia feature. Thus, the Fisher-Based Supervised Hill Climbing (FSHC) method is introduced. The contribution of this study is an optimized machine-learning-guided hill-climbing method that uses a classifier on a validation set as the objective function. A plateau mechanism also guided Hill Climbing exploration, not by a single Fisher point but by the neighboring subsets. The dataset used contains the graphomotor slant line task from 119 children aged 8-15 years (47.5% diagnosed with dysgraphia), with 10000 to 50000 data points per user. It is organized into kinematic, spatial, dynamic, and temporal features, yielding 117 sub-features. A stratified 5-fold cross-validation is set for training and testing, reaching 21 features. Comparative test—Linear SVM, SVM RBF, Sigmoid SVM, Polynomial SVM, Random Forest, AdaBoost, KNN, Decision Tree, Gradient Boosting, Gaussian Naive Bayes, and Gaussian Classifier—showed that linear SVM achieves the best performance with a weighted average precision, recall, and F1 score of 0.93. Linear SVM also outperformed the three approaches: no feature selection, the traditional Fisher, and machine-learning-based feature selection (weighted KNN and SVM). It can be concluded that the proposed method is more robust than the state of the art by highlighting key points for avoiding overfitting.
Intelligent Modelling of Electromechanical Piezoelectric Actuator Mohammed, Mohammed Jawad; Abtan, Akeel; Jawad, Raheel
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.v8i2.15020

Abstract

This research models a piezoelectric actuator using ARX model with particle swarm optimization method. System modeling is a crucial step in engineering for understanding system behavior and implementing control strategies especially when employing intelligent and precise modeling techniques. This research contributes by introducing a new type of modeling in this field by employing the Autoregressive with eXogenous inputs (ARX) model and the estimation parameters calculated by PSO method. In current work, 0 to 35 V as sinusoidal frequency wave at 1.2 Hz supplied to piezoelectric actuator (PZT) to collect the vibration as an output for the system using an accelerometer sensor. Then, double integration was employed to collect the displacement data. One side of the PZT was fixed, while the other was left free. MATLAB program 2022b employed to build the ARX model and manage the particle swarm optimization (PSO) variables such as swarm size and iterations to obtain the transfer function that represents dynamic behavior through input and output readings. The results appeared effectively of PSO and representing the accurate transfer function in continuous and discrete times. The PZT validated by recorded the minimum mean square error (MSE) up to 3.447×10-6 and the system behavior was within the confident limit at 95 % before and after the modeling process. The developed model can be used to design a robust vibration controller in the future or for energy harvesting.
K-Nearest Neighbors for Smart Solution Transportation: Prediction Distance Travel and Optimization of Fuel Usage and Charging Recommendations for ICE Vehicles Based in Surabaya Baskoro, Farid; Aribowo, Widi; Shehadeh, Hisham; Zangana, Hewa Majeed; Putro, Wahyu Sasongko; Dwiyanti, Sri; Nurdiansyah, Aristyawan Putra
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.v8i2.15068

Abstract

Surabaya ranks 9th in Southeast Asia and 44th globally in the TomTom Traffic Index, with an average travel time of ±22 minutes for a 10 km distance, longer than Jakarta’s ±20 minutes. Given these traffic conditions, this study examines the application of the K-Nearest Neighbors (KNN) algorithm to predict vehicle travel distance based on remaining fuel consumption and provides recommendations for the nearest Gas Station (SPBU) based on the predicted distance. The study seeks to provide accurate distance predictions and recommend the nearest Gas Station (SPBU) for users based on fuel consumption and the predicted route, helping to navigate Surabaya’s congested traffic efficiently. The data used includes various levels of fuel consumption: 0.02, 0.06, 0.10, 0.14, 0.16, 0.20, and 0.24 liters for engines of 110, 125, and 150 cc. The model evaluation results, using three metrics: MAE, MAPE, and RMSE show that KNN performs excellently at low fuel consumption levels. At a consumption rate of 0.02 liters, the model produces a low MAE of 0.347, MAPE of 31.21%, and RMSE of 0.40, indicating minimal prediction error. The model's performance remains consistent at a consumption of 0.06 liters with MAE of 0.330, MAPE of 9.90%, and RMSE of 0.41, demonstrating a high level of accuracy. Technically, the implementation of this model can help reduce traffic congestion by directing vehicles to the nearest gas stations, thereby minimizing sudden stops on the road, improving traffic flow, and reduce wasted time spent searching for distant gas stations.
An Integrated and Lightweight Control Framework for Solar-Powered Assistive Robots: Combining Adaptive Fuzzy Energy Management, Multi-Modal HRI, and Secure Communication Altufaili, Abulfadhel Amer Saihood; Shleej, Dunya Mohammed
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.v8i2.15190

Abstract

The current assistive robotics platforms tend to be unable to keep up with the unpredictable nature of renewable sources of energy, the lack of processing capacity, and the growing security risks. In this work, the integrated control architecture is introduced where the aspects of energy management, computation efficiency, and interaction reliability are considered in one and lightweight framework. The study paper contribution is the creation of a single architecture that integrates adaptive fuzzy energy control with multi-channel eye-gaze communication and IoT-based security, tailored to compute economically and resource-limited microcontroller processor designs. The approach combines the Adaptive Fuzzy Energy Manager (AFEM) of real-time power operation, Multi-Modal Control Interface (MMCI) based on electrooculography (EOG) in addition to the depth estimation of vision, and Lightweight Secure Communication Layer (LSCL) that employs 128-bit permutation-based encryption. The experimental test of the system was performed with a robotic arm of the 4-DOF with 150W PV-battery unit powered by an Arduino Mega. Experimental results demonstrate that the overall accuracy of EOG command classification was high at 97.8% and the location of the end-effector was positioned precisely with an error of less than 2 mm. AFEM values were sufficient to ensure state-of-charge of the battery was not less than 88 percent under varying sun irradiance and security layer was maintained to give low processing latency of less than 5 ms. The suggested framework is highly operational efficient and also interaction reliable rendering it applicable to deploy in the resource constrained resources in assistive and telemedicine settings.
CNN-Based Transfer Learning Models for Histopathological Detection of Non-Hodgkin Lymphoma on Histopathological Images Affan, Aghnia Hasya; Basari, Basari
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.v8i2.15207

Abstract

More than 85.720 new cases and 21.000 fatalities from lymphoma were reported globally in 2021. This type of cancer can spread through the body using the lymphatic system and then enter the blood. Since lymphoma affects the lymphatic system, it can be hard to diagnose correctly because there are many different subtypes, such as Mantle Cell Lymphoma (MCL), Follicular Lymphoma (FL), and Chronic Lymphocytic Leukemia (CLL). The diagnostic complexity of lymphoma highlights the need for more accurate and reliable automated diagnostic methods. This research proposes a transfer learning approach employing pre-trained Convolutional Neural Network (CNN) models using DenseNet-201, Xception, and ResNet-50, for lymphoma subtype classification. The dataset consists of microscopic histopathology images from three lymphoma classes (MCL, FL, and CLL). Each image was resized and segmented into 24 non-overlapping patches, followed by Macenko stain normalization and data augmentation. Model performance was evaluated using a random sampling with a fixed random seed train–validation–test split, and validated using cross-validation method. The proposed approach achieved classification accuracies of 96.7% for DenseNet-201, 97.15% for Xception, and 96.3% for ResNet-50. These results indicate that deeper architectures with efficient feature reuse and depthwise separable convolutions improve the detection of subtle morphological differences among lymphoma subtypes. Despite limitations related to dataset size and external validation, the findings demonstrate the potential of transfer learning-based CNN models as decision-support tools for lymphoma diagnosis.
Implementation of Vehicle Ad Hoc Networks for TPBFT on Latency and Fault Tolerance in Blockchain Systems Mezher, Liqaa Saadi; Abbass, Ayam Mohsen; Saleh, Muna Hadi
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.v8i2.15227

Abstract

In this paper, examines the combination of Vehicle Ad hoc Networks (VANETs) and a new consensus mechanism called Trust Practical Byzantine Fault Tolerance (TPBFT) that is aimed at improving latency and fault tolerance of decentralized vehicular networks. The VANETs are described by dynamic topology and mobile node and pose special security as well as reliability issues especially in scalable networks. The conventional Byzantine Fault Tolerance (BFT) protocols are ineffective because they incur communication overhead and scaling problems. This paper suggests TPBFT as a powerful consensus mechanism that is suitable to use in vehicular networks and is effective even when malicious or malfunctioning nodes are involved. To model real-life traffic patterns and communication scenarios, the research methodology presupposes extensive simulations based on Simulation of Urban Mobility (SUMO) tool and real-world Open Street Map (OSM) data with the help of the Python program. The performance of TPBFT is strictly tested and compared to the classic Practical Byzantine Fault Tolerance (PBFT) protocol through the analysis of the consensus latency, system throughput, and fault tolerance resilience. The findings indicate TPBFT has a shorter consensus latency (16 to 28 ms) and a greater throughput compared to PBFT and was more effective in time-constrained vehicular usage. The present work makes TPBFT an effective decentralized mechanism that allows achieving low latency, high throughput, and high resistance to Byzantine failures, offering a safe platform to deploy the blockchain technology in smart transportation systems. The optimization of the energy consumption profile of network nodes, as well as the refinement of the consensus process on the application of blockchain-based VANET architecture into practice, will be the subject of future research.

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