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 295 Documents
 25   26   27   28   29 
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.
Hybrid Stacking of Multilayer Perceptron, Convolutional Neural Network, and Light Gradient Boosting Machine for Short-Term Load Forecasting Nguyen, Trung Dung; Tuan, Nguyen Anh
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14410

Abstract

Short-term load forecasting (STLF) is essential for scheduling, dispatch, and demand-side management. Real-world load series exhibit rapid local fluctuations and calendar or exogenous influences that challenge single-model approaches. This study proposes a hybrid stacking framework combining a Multilayer Perceptron (MLP), a 1-D Convolutional Neural Network (CNN), and a Light Gradient Boosting Machine (LightGBM), integrated through a ridge-regression meta-learner. The CNN extracts local temporal patterns from sliding windows of the load series, and the MLP processes tabular features such as lags, rolling statistics, and calendar/holiday indicators. At the same time, LightGBM captures nonlinear interactions in the same feature space. Base learners are trained using a rolling TimeSeriesSplit to avoid temporal leakage, and their out-of-fold predictions are used as inputs for the meta-learner. Early stopping regularizes the neural models. Experimental backtests on Queensland electricity demand data (89,136 half-hourly samples) demonstrate that the stacked model achieves markedly lower forecasting errors, with MAPE ≈ 0.81%, corresponding to a 24% reduction compared to CNN (MAPE ≈ 1.07%) and a 32% reduction compared to MLP (MAPE ≈ 1.19%). Regarding runtime, LightGBM is the fastest (25s) but least accurate, while the stacked model requires longer computation (2488s) yet delivers the most reliable forecasts. Overall, the proposed framework balances accuracy and robustness, and it is modular, reproducible, and extensible to additional exogenous inputs or base learners.
Comparison of Machine Learning Algorithms with Feature Engineering for Epileptic Seizure Prediction Based on Electroencephalogram (EEG) Signals Ibrahim, Sutrisno; Rahutomo, Faisal; Henda, Reihan; Aljalal, Majid
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13145

Abstract

Epilepsy is a neurological disorder marked by recurrent seizures, which can greatly reduce patients' quality of life. Early and accurate seizure prediction is essential for effective clinical intervention and patient safety. This study proposes and evaluates a seizure prediction system using EEG signals processed through machine learning techniques combined with optimized feature extraction methods. The research contribution is the comprehensive comparative analysis of classifier-feature pairs for identifying the most effective configuration for seizure prediction tasks. Three classifiers—Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—were systematically compared, each combined with precisely engineered feature extraction methods, including Common Spatial Pattern (CSP), Discrete Wavelet Transform (DWT), statistical features, and frequency domain features. EEG data from seven patients, totaling approximately 68 hours with 40 seizure events, were obtained from the Children's Hospital Boston database. The results demonstrate that XGBoost with CSP features achieved the highest overall accuracy at 88% and specificity at 88%, while XGBoost with DWT features reached the highest sensitivity at 87%. Additional metrics including F1-score (0.85) and AUC-ROC (0.91) confirmed XGBoost's superior performance. Comparison with five recent studies showed our approach offers a 3-5% improvement in accuracy and sensitivity. These findings highlight the critical impact of both classifier selection and feature engineering in improving EEG-based seizure prediction, with implications for developing real-time monitoring systems despite challenges in clinical implementation due to inter-patient variability.
Bi-LSTM and Attention-based Approach for Lip-To-Speech Synthesis in Low-Resource Languages: A Case Study on Bahasa Indonesia Setyaningsih, Eka Rahayu; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Kristian, Yosi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14310

Abstract

Lip-to-speech synthesis enables the transformation of visual information, particularly lip movements, into intelligible speech. This technology has gained increasing attention due to its potential in assistive communication for individuals with speech impairments, audio restoration in cases of missing or corrupted speech signals, and enhancement of communication quality in noisy or bandwidth-limited environments. However, research on low-resource languages, such as Bahasa Indonesia, remains limited, primarily due to the absence of suitable corpora and the unique phonetic structures of the language. To address this challenge, this study employs the LUMINA dataset, a purpose-built Indonesian audio-visual corpus comprising 14 speakers with diverse syllabic coverage. The main contribution of this work is the design and evaluation of an Attention-Augmented Bi-LSTM Multimodal Autoencoder, implemented as a two-stage parallel pipeline: (1) an audio autoencoder trained to learn compact latent representations from Mel-spectrograms, and (2) a visual encoder based on EfficientNetV2-S integrated with Bi-LSTM and multi-head attention to predict these latent features from silent video sequences. The experimental evaluation yields promising yet constrained results. Objective metrics yielded maximum scores of PESQ 1.465, STOI 0.7445, and ESTOI 0.5099, which are considerably lower than those of state-of-the-art English systems (PESQ > 2.5, STOI > 0.85), indicating that intelligibility remains a challenge. However, subjective evaluation using Mean Opinion Score (MOS) demonstrates consistent improvements: while baseline LSTM models achieve only 1.7–2.5, the Bi-LSTM with 8-head attention attains 3.3–4.0, with the highest ratings observed in female multi-speaker scenarios. These findings confirm that Bi-LSTM with attention improves over conventional baselines and generalizes better in multi-speaker contexts. The study establishes a first baseline for lip-to-speech synthesis in Bahasa Indonesia and underscores the importance of larger datasets and advanced modeling strategies to further enhance intelligibility and robustness in low-resource language settings.
Optimal PID Controller Based on Different Modified Grasshopper Optimization Algorithm for Nonlinear Single-Input Single-Output System Flaih, Aliaa A.; Karam, Ekhlas H.; Mohammed, Yousra A.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14394

Abstract

This paper presents a comparative study of the Grasshopper Optimization Algorithm (GOA) with three suggested modified versions—Levy Flight GOA (LFGOA), Dynamic Attraction-Repulsion GOA (DARGOA), and Chaotic GOA (CHGOA)—for tuning Proportional-Integral-Derivative (PID) controller parameters in a nonlinear Single-Input Single-Output (SISO) system. The research contribution is the development and evaluation of CHGOA, which aims to improve convergence speed and transient response stability. The methodology employs exploratory and exploitative mechanisms of each algorithm to optimize PID parameters based on six objective functions. Performance metrics include rise time, settling time, overshoot, peak value, and best fitness obtained from MATLAB/Simulink simulations. A second-order Mass-Spring-Damper (MSD) system is used as a representative nonlinear SISO system. Simulation results indicate that the proposed CHGOA consistently achieves lower fitness values, faster convergence, and stable transient responses compared to LFGOA, DARGOA, and standard GOA, under the tested objective functions. While LFGOA and DARGOA show competitive performance in traditional error metrics, standard GOA exhibits slower convergence in simulation scenarios. In this paper, the performance of the MSD system controlled by the proposed optimal PID with GOAs was also compared with the performance of this system with Nonlinear PIDs (NPIDs) which proposed by previous studies. The comparison results showed the efficiency of our proposed controllers in improving the performance of the MSD system, especially the CHGOA. Overall, the proposed CHGOA provides an effective balance between error minimization, convergence speed, and transient response performance, making it suitable for high-precision real-time applications.
A Hybrid PI–SOSM Control Strategy with Disturbance Observer for Enhanced Dynamic Response of IM Drives Pham, Thanh Tinh; Pham, Ngoc Thuy
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14458

Abstract

This paper proposes a novel hybrid field-oriented control (FOC) strategy for high-performance induction motor (IM) drives, integrating a conventional Proportional–Integral (PI) controller in the speed loop and a Super-Twisting Second-Order Sliding Mode (SOSM) controller in the current loop. The main novelty lies in combining a sliding mode disturbance observer (OB) with a hybrid PI–SOSM structure, enabling real-time estimation and compensation of unknown load torque. The estimated torque is transformed into an equivalent disturbance current, which is directly added to the torque-producing current reference, thereby achieving feedforward disturbance rejection. The novel hybrid structure achives the improved dynamic response and robustness through self-compensated torque disturbance using OB, reduced chattering in current regulation via SOSM, and maintaining PI simplicity in the outer speed loop. Extensive simulation results by MATLAB/Simulink sotfware demonstrates that the hybrid controller offers superior dynamic performance, enhanced robustness against parameter uncertainties and load disturbances, and significantly reduced chattering effects compared with conventional PI–PI FOC.
Smart Cold Storage Based on Photovoltaic with Adaptive Fuzzy Control Approach for Guard Quality of Fish Catch on Fishing Vessels Findiastuti, Weny; Umam, Faikul; Sulaiman, Yoga Aulia; Thinakaran, Rajermani; Dafid, Ach.; Andriansyah, Adi; Yusuf, Ahcmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14508

Abstract

This research is motivated by the importance of maintaining the quality of fish catches on fishing vessels, which generally experience a decline in quality due to suboptimal conventional fish storage systems and limited energy supplies at sea. To address these challenges, the development of renewable energy-based cold storage technology through a Solar Power Plant (PLTS) or Photovoltaic system is needed. This research aims to design a PLTS-based smart cold storage system capable of optimally maintaining temperature stability using the Adaptive Fuzzy Control method. It is hoped that fish quality can be maintained and the economic value of fishermen's catches can be increased. This research uses an experimental approach through the design, implementation, and testing of a fuzzy logic-based adaptive control system in real-time. The performance results are then evaluated in maintaining the temperature stability of the cooling room and the efficiency of electrical energy sourced from solar panels. It is hoped that this research can provide real solutions for fishermen, support the economic independence of the fisheries sector, and support the achievement of sustainable development targets (SDGs) 7, 9 12, and 14. During the 24-hour test, the Adaptive Fuzzy Control system in a solar-based refrigerator demonstrated consistent performance in maintaining temperature stability (standard deviation σ = 3.28 °C–3.45 °C). The average refrigerator temperature was recorded at -5.44 °C with a range of -0.9 °C to -12 °C, which remains acceptable for marine fish preservation under superchilling and mild freezing conditions. The battery capacity was at an average of 89.95%, decreasing when there was no power supply and then increasing again during charging, thus reflecting adaptive energy management. The average charging speed was 3.14 A, with a peak of up to 15.6 A at 7–8 hours, then decreasing gradually as the battery was full to prevent overcharging. These findings confirm that the proposed system effectively balances cooling performance and renewable energy utilization. The use of solar photovoltaic energy directly supports SDG 7 (Affordable and Clean Energy), while system innovation and energy optimization align with SDG 9 (Industry, Innovation, and Infrastructure). The prototype demonstrates stable and efficient operation, and the design concept is scalable for practical implementation on small to medium-sized fishing vessels. A preliminary cost analysis indicates up to 50% lower operating costs compared to conventional diesel refrigeration systems.
Exploring Teachers' Perspectives on Culturally Responsive Teaching in Stoichiometry Learning Oriented to Green Chemistry Fitri, Hajidah Salsabila Allissa; Wiyarsi, Antuni; Widarti, Hayuni Retno; Yamtinah, Sri; Pratiwi, Yunilia Nur
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14520

Abstract

This study explores high school chemistry teachers’ perceptions of culturally responsive teaching (CRT) in stoichiometry learning with an orientation toward green chemistry principles. CRT provides a framework for integrating local culture, while green chemistry oriented highlights sustainability in learning abstract topics such as stoichiometry. This qualitative descriptive study involved nine Indonesian chemistry teachers selected purposively from active high school chemistry teachers familiar with stoichiometry and green chemistry. Although the sample size was limited, it provided data saturation and in-depth insights from teachers’ experiences. Data were collected through an open-ended questionnaire covering seven aspects, including teachers’ understanding of CRT, integration of local culture and green chemistry orientation in stoichiometry teaching, and perceived needs. Thematic analysis identified six themes related to teaching barriers, instructional practices, teachers’ CRT understanding, cultural integration, green chemistry orientation, and professional development needs. While teachers expressed strong support for CRT with a green chemistry orientation, implementation remains limited by structural and pedagogical constraints. The study underscores the importance of targeted support to empower teachers in fostering culturally responsive and sustainability-oriented chemistry learning, while acknowledging limitations related to the Indonesian context and the potential for social desirability bias in self-reported data.

Page 27 of 30 | Total Record : 295

 25   26   27   28   29 

Filter by Year

2019 2026


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