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Alfian Maarif
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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 364 Documents
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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; Ridho Hendra Yoga Perdana; 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.

Page 37 of 37 | Total Record : 364

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