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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jejak digital: Jurnal Ilmiah Multidisiplin
Tri Wrahatnolo
State University of Surabaya
Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Voltage Drop on High-Rise Buildings Using The Analytical Hierarchy Process (AHP) Method Muhammad Benjamin Reza; Unit Three Kartini; Tri Wrahatnolo; Tri Rijanto
Jejak digital: Jurnal Ilmiah Multidisiplin Vol. 2 No. 5 (2026): AGUSTUS-SEPTEMBER
Publisher : INDO PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/xr7h2376

Abstract

The AHP modeling method in this study was conducted by weighting six main technical criteria: Voltage Drop Magnitude (C1), Voltage Drop Percentage (C2), Load Magnitude (C3), Distance from Source (C4), Average Current (C5), and Line Impedance (C6). The analysis results show that the most dominant factor influencing voltage drop is voltage quality, with the highest criteria weights being Voltage Drop Percentage (35.7%) and Voltage Drop Magnitude (28.3%). Based on the global score index calculation, the classification of four alternative loads is as follows: The FJM Office (A1) ranks first with a score of 50.9% and is interpreted as an "Influential" load due to its voltage drop of 5.35%, which technically exceeds the standard operational limit. The AJG Office (A2) scored 23.6%, interpreted as "Moderately Influential," while the Workshop (A4) and the Security Post Office (A3) scored 16.9% and 8.2%, respectively, interpreted as "Less Influential”.
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.
Co-Authors Aristyawan Putra Nurdiansyah Farid Baskoro Fendi Achmad Hewa Majeed Zangana Hisham A. Shehadeh Muhammad Benjamin Reza Puput Wanarti Rusimamto Tri Rijanto Unit Three Kartini