cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Alfian Maarif
Contact Email
alfianmaarif@ee.uad.ac.id
Phone
-
Journal Mail Official
biste@ee.uad.ac.id
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 25 Documents
 1   2   3 
Search results for , issue "Vol. 7 No. 3 (2025): September" : 25 Documents clear
Transfer Learning Models for Precision Medicine: A Review of Current Applications Pamungkas, Yuri; Aung, Myo Min; Yulan, Gao; Uda, Muhammad Nur Afnan; Hashim, Uda
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14286

Abstract

In recent years, Transfer Learning (TL) models have demonstrated significant promise in advancing precision medicine by enabling the application of machine learning techniques to medical data with limited labeled information. TL overcomes the challenge of acquiring large, labeled datasets, which is often a limitation in medical fields. By leveraging knowledge from pre-trained models, TL offers a solution to improve diagnostic accuracy and decision-making processes in various healthcare domains, including medical imaging, disease classification, and genomics. The research contribution of this review is to systematically examine the current applications of TL models in precision medicine, providing insights into how these models have been successfully implemented to improve patient outcomes across different medical specialties. In this review, studies sourced from the Scopus database, all published in 2024 and selected for their "open access" availability, were analyzed. The research methods involved using TL techniques like fine-tuning, feature-based learning, and model-based transfer learning on diverse datasets. The results of the studies demonstrated that TL models significantly enhanced the accuracy of medical diagnoses, particularly in areas such as brain tumor detection, diabetic retinopathy, and COVID-19 detection. Furthermore, these models facilitated the classification of rare diseases, offering valuable contributions to personalized medicine. In conclusion, Transfer Learning has the potential to revolutionize precision medicine by providing cost-effective and scalable solutions for improving diagnostic capabilities and treatment personalization. The continued development and integration of TL models in clinical practice promise to further enhance the quality of patient care.
Design and Application of a Cyber Physical Based Data Logger System for Charging Stations Rahutomo, Faisal; Nugraha, Bagus Putra; Mekonnen, Atinkut Molla; Ariyo, Bashiru Olalekan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13266

Abstract

The rapid advancement of technology, particularly in transportation, has led to a growing public interest in electric vehicles. Government support, exemplified by Presidential Regulation No. 55 of 2019, further encourages this shift. With more electric vehicles on the road, the need for adequate charging infrastructure is critical. This research aims to design, test, and implement a charging device that records electric vehicle usage, displays data on an LCD, and allows monitoring through a website. Using the research and development (R&D) method, a highly effective design was developed. The data recording system employs the PZEM-004T sensor and ESP32 microcontroller to send data to a database. Validation tests showed high accuracy and precision, with current accuracy at 98.79% and precision at 99.24%, and voltage accuracy at 99.59% and precision at 99.87%. The device was installed in the basement of UPT TIK UNS and tested with three electric vehicles, each with different power requirements. The average power growth every ten minutes was 0.063 kWh for the first vehicle, 0.164 kWh for the second, and 0.139 kWh for the third. These results demonstrate that the device functions well, the design is successful, and it provides consistent, accurate, and precise energy growth measurements.
Metaheuristic-Driven Optimisation of Support Vector Regression Models for Precision Control in Unmanned Aerial Vehicle Systems Marhoon, Hamzah M.; Omar, Rasha Khalid; Al-Rammahi, Hussein; Al-Tahir, Sarah O.; Basil, Noorulden; Tarik, Benmessaoud Mohammed; Agajie, Takele Ferede
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14251

Abstract

Unmanned Aerial Vehicle (UAV) systems are deployed in dynamic and uncertain environments where many traditional control structures, including Proportional–Integral–Derivative (PID) and Linear Quadratic Regulator (LQR) controllers, are unable to provide stability and adaptation. In order to overcome these shortcomings, this work presents a hybrid Support Vector Regression (SVR) model optimised with the Eagle Strategy-Particle Swarm Optimisation (ES-PSO). The proposed framework is tested with high-fidelity simulated flight data on a quadcopter platform, in which throttle, pitch, roll and yaw are provided as control variables and altitude, velocity and orientation are provided as outputs. The ES-PSO algorithm is an algorithm that optimises the global and local hyperparameters of the SVR and makes it more effective at capturing nonlinear dynamics of the input-output process under both nominal and perturbed flight conditions. To compare with benchmarking, standalone SVR, Neural Networks, Decision Trees, Naive Bayes and K-Nearest Neighbour models were executed using the same simulation parameters with no metaheuristic optimisation, and it was made fair. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Percentage Error (MPE) quantitative assessments illustrate that the ES-PSO-SVR model has the lowest error in prediction and the highest tracking accuracy compared to all baseline techniques. These results demonstrate how metaheuristic-based learning systems can be used to drive forward the creation of adaptive and intelligent UAV control systems that can perform effectively in challenging operational conditions.
Bayesian-Optimized MLP-LSTM-CNN for Multi-Year Day-Ahead Electric Load Forecasting Tuan, Nguyen Anh; Nguyen , Trung Dung
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14345

Abstract

Accurate long-term electric load forecasting—multi-year, day-ahead peak-load prediction—is critical for planning, operations, and policy. While traditional statistical and shallow machine-learning methods often struggle with nonlinear and multi-scale temporal patterns, deep learning offers promising alternatives. This study conducts a systematic, controlled comparison of three architectures—Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—within a unified Bayesian hyperparameter optimization protocol using daily peak-load data from the New South Wales (NSW) electricity market, 2015–2021, with a 365-day look-back window. Under identical data splits, objective, and search procedures, CNN delivers the best accuracy across all metrics (MAE = 699, MSE = 791,838, RMSE = 890, MAPE = 7.53%), MLP performs slightly worse, and LSTM yields the most significant errors alongside the most extended runtime. These results indicate that, under consistent tuning and a one-year context window, CNN captures local variations more effectively than the recurrent alternative in this setting. The research contribution of this study is a fair, empirical benchmark of widely used deep models (MLP, CNN, LSTM) for multi-year, day-ahead peak-load forecasting under a single Bayesian optimization framework, offering practical guidance for model selection. Reproducibility is facilitated by fixed random seeds and comprehensive configuration/trace logging. Limitations include an intentionally univariate design (no exogenous variables), a focus on learned architectures rather than naïve baselines, and the absence of uncertainty quantification; future work will extend to multivariate inputs (e.g., weather and calendar effects), hybrid CNN–LSTM and Transformer-based models, and broader baseline and robustness evaluations.
Accurate Crowd Counting Using an Enhanced LCDANet with Multi-Scale Attention Modules Abeuov, Nurmukhammed; Absatov, Daniyar; Mutaliyev, Yelnur; Serek, Azamat
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14391

Abstract

Accurate crowd counting remains a challenging task due to occlusion, scale variation, and complex scene layouts. This study proposes ME-LCDANet, an enhanced deep learning framework built upon the LCDANet backbone, integrating multi-scale feature extraction via Micro Atrous Spatial Pyramid Pooling (MicroASPP) and attention refinement using CBAMLite modules. A preprocessing pipeline with Gaussian-based density maps, synchronized augmentations, and a dual-objective loss function combining density and count supervision supports effective training and generalization. Experimental evaluation on the ShanghaiTech Part B dataset demonstrates a Mean Absolute Error (MAE) of 11.50 (95% CI: 10.20–12.91) and a Root Mean Squared Error (RMSE) of 11.54 (95% CI: 10.26–12.99). Training dynamics indicate steadily declining loss and reduced validation MAE, while gradient norm analysis suggests reliable convergence. Comparative results show that, although CSRNet and SaNet achieve slightly lower MAE, ME-LCDANet attains a notably reduced RMSE, reflecting robustness against large prediction deviations. While the study focuses on a single benchmark dataset, the proposed architecture offers a promising approach for robust crowd counting in diverse scenarios.

Page 3 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): February Vol. 7 No. 4 (2025): December Vol. 7 No. 3 (2025): September Vol. 7 No. 2 (2025): June Vol. 7 No. 1 (2025): March Vol. 6 No. 4 (2024): December Vol. 6 No. 3 (2024): September Vol. 6 No. 2 (2024): June Vol. 6 No. 1 (2024): March Vol. 5 No. 4 (2023): December Vol. 5 No. 3 (2023): September Vol. 5 No. 2 (2023): June Vol. 5 No. 1 (2023): March Vol. 4 No. 3 (2022): December Vol. 4 No. 2 (2022): Agustus Vol. 4 No. 1 (2022): April Vol. 3 No. 3 (2021): Desember Vol 3, No 2 (2021): Agustus Vol. 3 No. 2 (2021): Agustus Vol 3, No 1 (2021): April Vol. 3 No. 1 (2021): April Vol. 2 No. 3 (2020): Desember Vol 2, No 3 (2020): Desember Vol. 2 No. 2 (2020): Agustus Vol 2, No 2 (2020): Agustus Vol 2, No 1 (2020): April Vol. 2 No. 1 (2020): April Vol 1, No 3 (2019): Desember Vol. 1 No. 3 (2019): Desember Vol 1, No 2 (2019): Agustus Vol. 1 No. 2 (2019): Agustus Vol 1, No 1 (2019): April Vol. 1 No. 1 (2019): April More Issue