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 378 Documents
 34   35   36   37   38 
Performance Evaluation of a Rabbit Manure-Based Biogas Power System: Slurry Dynamics, Energy Yield, and Conversion Efficiency As'ad Shidqy Aziz; Deshinta Arrova Dewi; Daeng Rahmatullah; Muhammad ‘Izzuddin Al-Qassam; Ayusta Lukita Wardani; Fithrotul Irda Amaliah; Onny Setyawati
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.v8i4.16870

Abstract

The increasing demand for sustainable energy has encouraged decentralized biogas-based power systems, yet a critical research gap remains regarding their field-scale integration and multi-parameter thermodynamic evaluations under real farming conditions. The research contribution is the field-scale operational integration and continuous performance evaluation of a 500 L rabbit manure biodigester coupled with a three-stage purification unit and a modified 1000 W generator set. Utilizing a transparent, reproducible mathematical framework, fresh rabbit manure was digested under a 37-day hydraulic retention time. Results revealed that the accumulated slurry occupied 35.5% of the biodigester volume, leaving 64.5% available as headspace for passive thermodynamic pressure management. The purified biogas successfully operated a 40 W barn lighting load for 12 h day⁻¹, generating a stable average electrical energy output of 0.485 kWh day⁻¹ and a specific energy yield of 0.202 kWh kg⁻¹ of fresh manure. The integrated system achieved a validated Specific Energy Consumption (SEC) value of 0.99 and an overall energy conversion efficiency of 64.7%. While this investigation is limited by its small-scale setup and a 31-day batch cycle, the practical implications demonstrate that this layout provides a viable, standalone template for circular waste management and rural energy independence, establishing an empirical baseline to motivate future automated or upscaled microgrid architectures.
Performance Evaluation of Sensor Data Filtering Methods for Signal Processing in TVET Learning Applications Farid Baskoro; Hisham A. Shehadeh; Hewa Majeed Zangana; Tri Wrahatnolo; Puput Wanarti Rusimamto; Fendi Achmad; Aristyawan Putra Nurdiansyah
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.v8i4.16883

Abstract

Technical and Vocational Education and Training (TVET) learning requires sensor measurement data that are stable, accurate, and easy to interpret. Raw LiDAR sensor data often contain fluctuations that may interfere with the readability results. This study employed an experimental-comparative design by comparing Moving Average, Median Filter, Savitzky-Golay, Butterworth, and Simple Kalman Filter. The data acquisition system used a VL53L0X LiDAR sensor and ESP32 microcontroller. Data processing was conducted in MATLAB on 10,500 samples at a sampling frequency of 50 Hz. The evaluation was carried out based on error metrics, signal stability, noise reduction, and filter responsiveness. The raw data had a standard deviation of 111.26 and still showed fluctuations that required reduction. A Greenhouse–Geisser-corrected repeated-measures ANOVA showed a significant effect of filtering method on segment-level residual RMSE, F(1.10,44.92)=26.23, p<0.001, partial η2=0.390. Bonferroni-adjusted comparisons showed that Savitzky–Golay produced significantly lower residual RMSE than the other methods, indicating stronger preservation of the raw-signal pattern. The results showed that Savitzky–Golay achieved the best overall trade-off, with the lowest residual deviation, the highest estimated SNR of 32.154 dB, and good pattern preservation without excessive smoothing. Butterworth and Simple Kalman provided stronger fluctuation reduction, although Kalman introduced greater deviation and a 39-sample delay. Moving Average offered simple smoothing, whereas the Median Filter was more suitable for impulsive noise and outliers. This study contributes a comparative evaluation of filtering methods from both signal-processing and TVET pedagogical perspectives, supporting filter selection based on smoothness, readability, noise reduction, and responsiveness in signal processing.
Dynamic Multi-Scale Ensemble iTransformer for Day-Ahead Electric Load Forecasting Tuan Anh Nguyen; Trung Dung Nguyen
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.v8i4.17107

Abstract

Accurate day-ahead electric load forecasting is essential for reliable power-system operation, yet high-resolution demand exhibits nonlinear short-term variations alongside strong daily and weekly recurrence. This study addresses the difficulty of representing these complementary temporal patterns within a single forecasting model. The research contribution is a validation-driven Dynamic Multi-Scale Ensemble iTransformer framework that integrates a tuned iTransformer with explicit daily, weekly, multi-week, and load-profile references, removes highly redundant candidates, and adaptively combines the retained forecasts across load states and forecast-horizon blocks. The framework uses only historical load observations and follows a chronological, leakage-free protocol for model tuning, candidate selection, weight optimization, strategy selection, and final testing. It was evaluated on New South Wales electricity demand data sampled at 5-minute intervals, using the previous 288 observations to forecast the next 288 observations. The proposed model achieved the best overall performance among the six evaluated methods. It yielded an MAE of 478.485 MW, an RMSE of 690.116 MW, a MAPE of 7.031%, an sMAPE of 6.718%, and an R² of 0.8484. Its MAPE was lower than those of Seasonal Naive (7.979%) and the standalone iTransformer (8.691%), corresponding to relative reductions of 11.88% and 19.10%, respectively. CNN, Persistence Naive, and LSTM achieved MAPEs of 12.419%, 17.596%, and 19.830%, respectively. These results show that explicit multi-scale temporal references and validation-optimized adaptive fusion complement the learned iTransformer representation, thereby improving deterministic day-ahead load forecasting accuracy.
Fixed-Time Synergetic Control Based Arctic Puffin Optimization for Knee-Exoskeleton Systems Huthaifa Al-Khazraji; Laith K. Majeed; Mohammed K. Hamzah; Ahmed Sameer Abdulmohsin
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.v8i4.17182

Abstract

Knee exoskeletons are proving to be highly effective tools for people with leg impairments, using external mechanical support to help their knees move more easily. However, to achieve an accurate trajectory tracking, nonlinearities and torque interaction due human–robot interaction can significantly affect the dynamic performance of the system. In this study, a fixed-time synergetic control (FTSC) strategy is proposed for the motion control of the knee-joint of an exoskeleton robot system. Furthermore, the performance of the proposed FTSC scheme is optimized using the Arctic Puffin Optimization (APO). A comparative study between the FTSC and conventional synergetic control (CSC) is carried out under step and sinusoidal motion-tracking scenarios. The results demonstrate the superior tracking performance of the proposed FTSC compared with the conventional CSC. The Integral Time of Absolute Error (IAE) performance index is selected as a quantitative measurement for improvements. The numerical data of the results reveal that the tracking error of the system controlled by the FTSC is reduced by 37.34% and 79.1% compared to that of the system controlled by the CSC for the unit step and sinusoidal signal inputs respectively. Furthermore, the FTSC demonstrated a substantial enhancement when a parameter variation was augmented in the simulation for the sinusoidal signal inputs.
An Innovation Approach for Feature Selection Medical Data Using Joint Fine-Tuning Fusion Graph Convolutional Network Dimas Chaerul Ekty Saputra; Irianna Futri; Elvaro Islami Muryadi
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.v7i4.11652

Abstract

This research addresses the challenge of feature selection in high dimensional medical datasets, where unnecessary or duplicated information can hide patterns and negatively impact model performance. The aim is to develop an efficient feature selection strategy using Fine-tuning Fusion Graph Convolutional Networks (GCNs) to enhance model accuracy and interpretability. The objectives include improving the medical data selection process, increasing generalization, and assisting healthcare professionals in making educated clinical decisions based on the most relevant factors. The study employs Joint Fine-Tuning Fusion Graph Convolutional Networks (GCNs) for feature selection in medical datasets. This approach entails creating several graphs to illustrate feature interrelations, amalgamating them into a cohesive representation, and optimizing the model to emphasize pertinent aspects. The L2-norm of the final embeddings dictates feature significance, directing the choice of the most critical features for enhanced predictive accuracy. The study's findings indicate that GCN-based feature selection improves classification accuracy, especially for the PIDD dataset, enhancing accuracy, precision, recall, and F1-score from 0.74 to 0.75. The Kidney Failure dataset exhibited near-perfect accuracy (0.99) prior to selection, whereas the heart disease dataset had a minor reduction in performance (from 0.81 to 0.80), highlighting the dataset-specific effects of feature selection. GCN-based feature selection improved classification performance, increasing the PIDD dataset's accuracy from 0.74 to 0.75, with no significant effect on the Kidney Failure dataset. Nonetheless, it somewhat diminished performance for the heart disease dataset. Subsequent study ought to enhance feature selection techniques by integrating dataset-specific optimizations and domain expertise to augment model precision and overall generalizability.
Mobile 360° Panoramic Training for Commercial Kitchen Safety: Usability and Learning Outcomes Asniyani Nur Haidar Abdullah; Mohd Khalid Mokhtar; Ikmal Faiq Albakri Mustafa Albakri; Reshmahdevi Janarthanan Nambiar; Mazni Saad; Muhammad Danial Haiqal Mohamad Najib
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.v7i4.13919

Abstract

Commercial kitchens are high-risk workplaces where staff routinely face hazards such as slips, burns, lacerations, and chemical exposure. Conventional classroom-based safety training often suffers from low engagement and weak retention, limiting preparedness for dynamic, high-pressure conditions. To address this, the present study developed and evaluated a mobile 360° panoramic training platform to enhance hazard awareness in commercial kitchens. Unlike fully modeled virtual reality (VR) simulations or generic training contexts, the platform delivers authentic kitchen imagery in dual modes—immersive via Google Cardboard and non-immersive via smartphone—balancing realism, accessibility, and cost efficiency. This exploratory quantitative study involved thirty semester-one culinary students (ages 18–23) from Kolej Komuniti Bukit Beruang, Melaka, recruited through a convenience sampling approach. Participants completed pre- and post-training hazard-identification tests and the System Usability Scale (SUS). Usability ratings were consistently high across ease of use, learnability, efficiency, and satisfaction (means 4.27–4.70). Hazard-identification scores increased significantly from 29.33 to 83.67; a paired-samples t-test confirmed the improvement (p < 0.001, d = 3.46). Participant feedback highlighted realism and accessibility as strengths, though reduced interactivity compared to full VR was noted. Findings align with prior VR-based training studies in healthcare and construction, suggesting that panoramic imagery can deliver comparable learning gains at lower cost and deployment effort. Limitations include the small, short-term sample, absence of a control group, and user-reported issues such as headset discomfort and accessibility concerns. Future research should examine longitudinal retention, controlled comparisons with traditional training, and scalability across diverse settings to establish broader real-world impact.
Deep Learning-based Channel State Estimation for V2V OFDM Communication: A Comparative Study of LSTM, BiLSTM, and GRU Networks Eman Rashedy; Mohamed Metwally Mahmoud; Alfian Ma'arif; Mohamed Hassan Essai; Kuruva Raju; Ehab K. I. Hamad
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.v7i4.14064

Abstract

CSE is crucial for OFDM systems to handle multipath fading in wireless channels. While CS techniques like SOMP are computationally efficient, their performance is limited by basis mismatch and noise sensitivity. This paper presents a comprehensive comparison between SOMP and DL approaches using LSTM, BiLSTM, and GRU networks for CSE in V2V communication. The performance of the proposed DL models is rigorously evaluated in a realistic V2V communication scenario utilizing the 3GPP standard vehicular channel model within an OFDM system, with estimation accuracy assessed based on MSE. Experimental results demonstrate that the DL architectures significantly outperform SOMP, achieving a reduction in MSE by up to 15 dB and a reduction in BER by up to three orders of magnitude at high SNRs while maintaining robust performance in high-mobility highway environments. The study establishes DL, particularly the efficient GRU model, as a superior paradigm for accurate and adaptive channel estimation in modern wireless communication systems, thereby contributing to safer and more reliable V2V communication essential for next-generation intelligent transportation systems. The proposed models are trained to accurately estimate the CSI, which is subsequently utilized for the final detection of the transmitted data.
Hybrid Vision Transformer for Brain and Lung Tumor Detection: A Multi-Modal Approach Using MRI (BraTS) and CT (LUNA16) Datasets Hewa Majeed Zangana; Mohammed Aquil Mirza; Sharyar Wani; Xinwei Cao; Marwan Omar
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.v7i4.14766

Abstract

The integration of artificial intelligence (AI) into medical imaging has transformed clinical diagnostics, yet existing CNN-based systems still struggle with capturing global spatial context and generalizing across modalities. This study addresses this gap by proposing a hybrid Vision Transformer (ViT) architecture for tumor detection in MRI and CT scans, evaluated on two benchmark datasets: BraTS (brain MRI) and LUNA16 (lung CT). The research contribution is a unified, end-to-end transformer model that processes heterogeneous modalities without traditional feature-level fusion. The proposed method incorporates convolutional layers for local feature extraction alongside transformer blocks for long-range dependency modeling. Extensive experiments demonstrate that our model achieves a 2.5% higher Dice score and 3.1% higher F1-score compared to state-of-the-art CNN-based baselines, with accuracy reaching 95.4% on BraTS and 93.6% on LUNA16. Attention-based heatmaps further enhance explainability by highlighting clinically relevant tumor regions. These results show that hybrid transformers offer a robust and interpretable framework for multi-modal tumor detection, paving the way for more reliable and transparent AI-assisted radiological diagnostics.
Hybrid Bayesian–Hyperband Optimization of a One-Dimensional Convolutional Neural Network for Short-Term Load Forecasting Trung Dung Nguyen; Nguyen Anh Tuan
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.v7i4.14824

Abstract

Short-term load forecasting (STLF) plays a crucial role in power system operation and electricity market planning. However, the forecasting accuracy of convolutional neural network (CNN) models strongly depends on properly tuned hyperparameters such as filter numbers, batch size, and loss function, while exhaustive tuning is computationally expensive. This paper proposes a one-dimensional CNN (1D-CNN) for STLF whose key hyperparameters are optimized by a hybrid Bayesian Optimization–Hyperband framework (BO-TPE-HB). The framework combines the Tree-structured Parzen Estimator (TPE), which guides the search towards promising regions of the hyperparameter space, with Hyperband’s multi-fidelity early-stopping strategy to terminate weak configurations early and save computation. The proposed approach is evaluated using half-hourly electricity load data from the Australian Energy Market Operator (AEMO) for New South Wales (NSW) and Victoria (VIC) from 2009 to 2014. Using a rolling-origin time-series cross-validation scheme on the training set and a chronologically separated hold-out test set, BO-TPE-HB explores 100 candidate configurations of a 4-layer 1D-CNN with a sliding window of 48 time steps. Model performance is assessed using MAE, MSE, RMSE, MAPE, and total hyperparameter optimization runtime. Experimental results show that the BO-TPE-HB–tuned CNN achieves test MAPE of 1.350% (MAE 105.98 MW, RMSE 143.71 MW) for NSW and 1.615% (MAE 89.13 MW, RMSE 124.25 MW) for VIC, outperforming Random Search, standalone TPE, and standalone Hyperband in terms of both prediction accuracy and computational efficiency, while requiring substantially less tuning time than plain Bayesian Optimization. These findings highlight that combining probabilistic search with resource-efficient early stopping provides a practical and reproducible way to enhance CNN-based STLF, and can be extended to multivariate inputs or other deep learning forecasting architectures in future work.
Performance Evaluation of ResNet50 and VGG16 in Automated Multi-Stage Breast Cancer Histopathology Grading Agoes Santika Hyperastuty; Anik Nur Handayani; Heru Wahyu Herwanto; Nor Salwa Damanhuriis
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.v8i5.15824

Abstract

Breast cancer histopathological grading is important for estimating tumor aggressiveness and supporting treatment planning, yet manual assessment can be influenced by observer variability. This study comparatively evaluates two convolutional neural network architectures, ResNet50 and VGG16, for automated multi-stage breast cancer histopathology grading based on the Nottingham histologic grading categories: Grade I, Grade II, and Grade III. The task was formulated as image-level classification using 1,742 hematoxylin and eosin-stained histopathological images obtained from 150 patients at an Anatomical Pathology Laboratory. All images were resized to 224 × 224 pixels, converted into normalized numerical RGB values, and augmented using horizontal and vertical flipping. The dataset was divided into training, validation, and testing subsets while considering patient-level separation to reduce data leakage. VGG16 was trained using stochastic gradient descent with a learning rate of 0.001, a batch size of 32 for training and 64 for testing, and 60 epochs. ResNet50 was trained using stochastic gradient descent and CrossEntropyLoss for 200 epochs, with raw logits in the final layer because CrossEntropyLoss internally applies softmax. Experimental results showed that ResNet50 achieved higher training accuracy (0.9735), higher validation accuracy (0.6792), lower training and validation loss (0.0809), and slightly higher recall (0.7095) than VGG16. Both models obtained the same test accuracy of 0.7590. These findings indicate that ResNet50 demonstrated stronger learning behavior and slightly better sensitivity, although external validation and class-wise analysis remain necessary before clinical application. The study also provides a controlled baseline for future CNN-based decision-support research in digital breast pathology settings and practice.

Page 37 of 38 | Total Record : 378

 34   35   36   37   38 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 5 (2026): October Vol. 8 No. 4 (2026): August Vol. 8 No. 3 (2026): June Vol. 8 No. 2 (2026): April 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