Performance Evaluation of Sensor Data Filtering Methods for Signal Processing in TVET Learning Applications
DOI:
https://doi.org/10.12928/biste.v8i4.16883Keywords:
LiDAR, TVET, Data Filtering, Signal Processing, SensorAbstract
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.
References
J. Cai and M. Kosaka, “Conceptualizing Technical and Vocational Education and Training as a Service Through Service-Dominant Logic,” Sage Open, vol. 14, no. 2, 2024, https://doi.org/10.1177/21582440241240847.
S. Park and H. Park, “Capacity Building of TVET Trainers in a Developing Country Through Practice-oriented Curriculum Implementation,” Journal of Technical Education and Training, vol. 17, no. 4, pp. 135–147, 2025, https://doi.org/10.30880/jtet.2025.17.04.011.
R. Parua and W. Yang, “The Driving Logic of Digital Transformation in TVET,” Vocation, Technology & Education, vol. 1, no. 2, p. e17, 2024, https://doi.org/10.54844/vte.2024.0590.
M. J. Gomez, J. A. Ruipérez-Valiente, and F. J. García Clemente, “Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study,” Sensors, vol. 23, no. 23, p. 9303, 2023, https://doi.org/10.3390/s23239303.
P. A. García-Tudela and J.-A. Marín-Marín, “Use of Arduino in Primary Education: A Systematic Review,” Educ. Sci. (Basel)., vol. 13, no. 2, p. 134, 2023, https://doi.org/10.3390/educsci13020134.
D. Hercog, T. Lerher, M. Truntič, and O. Težak, “Design and Implementation of ESP32-Based IoT Devices,” Sensors, vol. 23, no. 15, p. 6739, 2023, https://doi.org/10.3390/s23156739.
M. Bleier, “Sensor Cube: A Tool for Hands-on Learning of Sensor Data Processing,” IFAC-PapersOnLine, vol. 58, no. 16, pp. 23–28, 2024, https://doi.org/10.1016/j.ifacol.2024.08.456.
T. Panskyi, E. Korzeniewska, and A. Firych-Nowacka, “Educational Data Clustering in Secondary School Sensor-Based Engineering Courses Using Active Learning Approaches,” Applied Sciences, vol. 14, no. 12, p. 5071, 2024, https://doi.org/10.3390/app14125071.
L. G. Trujillo-Franco, H. F. Abundis-Fong, and J. C. Marin-Soriano, “An Open-Source Data Acquisition Platform for Teaching Vibration Analysis,” Computer Applications in Engineering Education, vol. 32, no. 5, p. e22753, 2024, https://doi.org/10.1002/cae.22753.
M. J. Silva, C. Gouveia, and C. A. Gomes, “The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects,” Sensors, vol. 23, no. 18, p. 7677, 2023, https://doi.org/10.3390/s23187677.
M. A. Hernández-Mustieles et al., “Wearable Biosensor Technology in Education: A Systematic Review,” Sensors, vol. 24, no. 8, p. 2437, 2024, https://doi.org/10.3390/s24082437.
X. Zhang, Y. Ding, X. Huang, W. Li, L. Long, and S. Ding, “Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms,” Sensors, vol. 24, no. 17, p. 5487, 2024, https://doi.org/10.3390/s24175487.
P. Sun, C. Sun, R. Wang, and X. Zhao, “Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review,” Sensors, vol. 22, no. 23, p. 9316, 2022, https://doi.org/10.3390/s22239316.
N. Lopac, I. Jurdana, A. Brnelić, and T. Krljan, “Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector,” Sensors, vol. 22, no. 16, p. 5946, 2022, https://doi.org/10.3390/s22165946.
D. Wisth, M. Camurri, and M. Fallon, “VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots,” IEEE Transactions on Robotics, vol. 39, pp. 309–326, Jul. 2022, https://doi.org/10.1109/TRO.2022.3193788.
S. Jiang et al., “Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration,” Comput. Electron. Agric., vol. 220, p. 108870, 2024, https://doi.org/10.1016/j.compag.2024.108870.
J. A. Ruipérez-Valiente, R. Martínez-Maldonado, D. Di Mitri, and J. Schneider, “From Sensor Data to Educational Insights,” Sensors, vol. 22, no. 21, p. 8556, 2022, https://doi.org/10.3390/s22218556.
M. Lötscher, N. Baumann, E. Ghignone, A. Ronco, and M. Magno, “Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study,” in 2023 IEEE International Conference on Omni-layer Intelligent Systems, 2023. https://doi.org/10.1109/COINS57856.2023.10189236.
T. T. Nguyen, V. T. Nguyen, and H. Kim, “Improvement of Accuracy and Precision of the LiDAR System Working in High Background Light Conditions,” Electronics (Basel)., vol. 11, no. 1, p. 45, 2021, https://doi.org/10.3390/electronics11010045.
V. G. Biju and others, “Assessing the Influence of Sensor-Induced Noise on Machine Learning Accuracy,” Sensors, vol. 24, no. 2, p. 330, 2024, https://doi.org/10.3390/s24020330.
L. Türkler and L. Ö. Akkan, “Noise Reduction Techniques for Sensor Data: Comparative Analysis of Kalman, Butterworth, Savitzky-Golay, Median, and Moving Average Filters for UWB-Based Position Estimation,” Celal Bayar University Journal of Science, vol. 21, no. 4, pp. 146–159, 2025, https://doi.org/10.18466/cbayarfbe.1682594.
M. H. Raju, “Filtering Eye-Tracking Data From an EyeLink 1000,” Vision, vol. 14, no. 3, p. 17, 2023, https://doi.org/10.3390/vision14030017.
A. E. Hurtado-Perez and others, “Use of Technologies for the Acquisition and Processing of Sensor Data: A Review,” Biomimetics, vol. 10, no. 5, p. 339, 2025, https://doi.org/10.3390/biomimetics10050339.
A. J. Kałka and A. M. Turek, “Searching for Alternatives to the Savitzky–Golay Filter in the Spectral Processing Domain,” Appl. Spectrosc., vol. 77, no. 4, pp. 426–432, 2023, https://doi.org/10.1177/00037028231154278.
M. H. Raju, L. Friedman, T. M. Bouman, and O. V Komogortsev, “Filtering Eye-Tracking Data from an EyeLink 1000: Comparing Heuristic, Savitzky–Golay, IIR, and FIR Digital Filters,” J. Eye Mov. Res., vol. 14, no. 3, p. 6, 2023, https://doi.org/10.16910/jemr.14.3.6.
G. Takács, E. Mikuláš, M. Gulan, A. Vargová, and J. Boldocký, “AutomationShield: An Open-Source Hardware and Software Initiative for Control Engineering Education,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 9594–9599, 2023, https://doi.org/10.1016/j.ifacol.2023.10.263.
F. Baskoro, I. G. P. A. Buditjahjanto, M. Rohman, D. A. S. Fridyatama, and A. P. Nurdiansyah, “Impact of Sample Size Variation on Moving Average Filter Performance for Stability and Accuracy in Ultrasonic Sensor Measurements,” TEM Journal, vol. 14, no. 2, pp. 1681–1688, 2025, https://doi.org/10.18421/TEM142-65.
F. Baskoro, M. Rohman, D. Arya, and A. Putra, “Optimizing Stability of Ultrasonic Sensor Readings: Study on Moving Average Filter Parameter Selection,” JOURNAL ZETROEM, vol. 7, no. 1, pp. 59–65, Mar. 2025, https://doi.org/10.36526/ztr.v7i1.3603.
N. Ariyanto, F. Baskoro, R. Firmansyah, L. Anifah, T. Wrahatnolo, and D. A. S. Fridyatama, “Trade-Off Analysis of Moving Average Filter in Light Sensors,” Engineering Science Letter, vol. 5, no. 02, pp. 103–109, Jun. 2026, https://doi.org/10.56741/IISTR.esl.002255.
N. Li and others, “An Improved Adaptive Median Filtering Algorithm for Radar Co-Channel Interference,” Sensors, vol. 22, no. 19, p. 7573, 2022, https://doi.org/10.3390/s22197573.
N. Hublikar and A. Gupta, “Comparative Analysis of Median, Gaussian, and Butterworth Filters Under Consistent and Varying Noise Conditions at Multiple Filter Sizes,” Jul. 2025, pp. 1–4. https://doi.org/10.1109/ICITEICS64870.2025.11341237.
M. M. Elgaud et al., “Digital Filtering Techniques for Performance Improvement of Golay Coded TDM-FBG Sensor,” Sensors, vol. 21, no. 13, p. 4299, 2021, https://doi.org/10.3390/s21134299.
Y. Rawash, B. Al-Naami, A. Alfraihat, and H. Owida, “Advanced Low-Pass Filters for Signal Processing: A Comparative Study on Gaussian, Mittag-Leffler, and Savitzky-Golay Filters,” Mathematical Modelling of Engineering Problems, vol. 11, pp. 1841–1850, Jul. 2024, https://doi.org/10.18280/mmep.110713.
L. Zhao, H. Yuan, K. Xu, J. Bi, and B. H. Li, “Hybrid network attack prediction with Savitzky–Golay filter-assisted informer,” Expert Syst. Appl., vol. 235, p. 121126, 2024, https://doi.org/10.1016/j.eswa.2023.121126.
C.-S. Huang and others, “An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems,” Batteries, vol. 9, no. 4, p. 198, 2023, https://doi.org/10.3390/batteries9040198.
D. Suescún-Díaz, G. Ule-Duque, and L. E. Cardoso-Páez, “Butterworth Filter to Reduce Reactivity Fluctuations,” Karbala International Journal of Modern Science, vol. 10, no. 1, pp. 91–98, 2024, https://doi.org/10.33640/2405-609X.3342.
O. J. Montañez and others, “Application of Data Sensor Fusion Using Extended Kalman Filter Algorithm for Identification and Tracking of Moving Targets from LiDAR–Radar Data,” Remote Sens. (Basel)., vol. 15, no. 13, p. 3396, 2023, https://doi.org/10.3390/rs15133396.
A. Chhabra and others, “Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations,” Sensors, vol. 21, no. 24, p. 8304, 2021, https://doi.org/10.3390/s21248304.
D. Voipan, A. E. Voipan, and M. Barbu, “Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions,” Sensors, vol. 25, no. 6, p. 1692, 2025, https://doi.org/10.3390/s25061692.
A. Tungatarova, G. Borankulova, A. Murzakhmetov, and B. Turarova, “Hybrid IoT-VIoT System for Real-Time Water-Level Monitoring Using Computer Vision,” Computers, vol. 15, no. 6, p. 373, 2026, https://doi.org/10.3390/computers15060373.
S. Komarizadehasl, B. Mobaraki, H. Ma, J.-A. Lozano-Galant, and J. Turmo, “Low-Cost Sensors Accuracy Study and Enhancement Strategy,” Applied Sciences, vol. 12, no. 6, 2022, https://doi.org/10.3390/app12063186.
L. Xiong, T. Zhang, A. Yuan, and Z. Zhang, “Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal,” World Electric Vehicle Journal, vol. 15, no. 6, p. 254, 2024, https://doi.org/10.3390/wevj15060254.
L. Huang, D. Li, H. Yan, K. Wang, and Z. Huang, “A Low-Complexity Forward–Backward Filtering Algorithm for Real-Time GNSS Deformation Monitoring at the Edge,” Electronics (Basel)., vol. 14, no. 12, p. 2388, 2025, https://doi.org/10.3390/electronics14122388.
M. Jung and D.-Y. Kim, “DEFT: A Dynamic Environmental Filtering and Thresholding Algorithm for Adaptive Headlamp Control Using Ride Height Sensors,” Electronics (Basel)., vol. 13, no. 23, p. 4788, 2024, https://doi.org/10.3390/electronics13234788.
S. Kumar, R. Raja, M. R. Mahmood, and S. Choudhary, “A Hybrid Method for the Removal of RVIN Using Self Organizing Migration with Adaptive Dual Threshold Median Filter,” Sens. Imaging, vol. 24, no. 1, p. 9, 2023, https://doi.org/10.1007/s11220-023-00414-9.
A. Kenioua, O. Djebili, and A. T. Brahim, “Hybrid Extreme Learning Machine for Real-Time Rate of Penetration Prediction,” J. Pet. Explor. Prod. Technol., vol. 15, p. 139, 2025, https://doi.org/10.1007/s13202-025-02048-x.
N. Ádám, D. Val’ko, Z. Balogh, B. Madoš, and J. Hurtuk, “Comparative Evaluation of Filtration Techniques for ECG Signal Denoising with Emphasis on Stationary Wavelet Transform,” Sci. Rep., vol. 15, p. 42514, 2025, https://doi.org/10.1038/s41598-025-26476-1.
D. Khan et al., “Robust Human Locomotion and Localization Activity Recognition over Multisensory,” Front. Physiol., vol. 15, p. 1344887, 2024, https://doi.org/10.3389/fphys.2024.1344887.
S. Ellens, D. L. Carey, P. B. Gastin, and M. C. Varley, “Accuracy of GNSS-Derived Acceleration Data for Dynamic Team Sport Movements: A Comparative Study of Smoothing Techniques,” Applied Sciences, vol. 14, no. 22, p. 10573, 2024, https://doi.org/10.3390/app142210573.
N. Zhang et al., “Kalman Filtering to Reduce Measurement Noise of Sample Entropy: An Electroencephalographic Study,” PLoS One, vol. 19, no. 7, p. e0305872, 2024, https://doi.org/10.1371/journal.pone.0305872.
T. Kruse, T. Griebel, and K. Graichen, “Adaptive Kalman Filtering: Measurement and Process Noise Covariance Estimation Using Kalman Smoothing,” IEEE Access, vol. 13, pp. 11863–11875, 2025, https://doi.org/10.1109/ACCESS.2025.3528348.
Y. Zhang et al., “Vehicle Dynamics Estimator Utilizing LSTM-Ensembled Adaptive Kalman Filter,” IEEE Transactions on Industrial Electronics, vol. 72, no. 5, pp. 5429–5439, May 2025, https://doi.org/10.1109/TIE.2024.3482011.
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Copyright (c) 2026 Farid Baskoro, Hisham A. Shehadeh, Hewa Majeed Zangana, Tri Wrahatnolo , Puput Wanarti Rusimamto, Fendi Achmad, Aristyawan Putra Nurdiansyah
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