<![CDATA[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.]]>
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