Trade-Off Analysis of Moving Average Filter in Light Sensors
Keywords:
Light Intensity, Light Sensor, Moving Average Filter, Noise Reduction, RMSE, Rise Time, Trade-OffAbstract
Light intensity measurements based on sensors often experience signal quality degradation due to noise interference. Although filtering methods are commonly used to reduce noise, improvements in signal stability are frequently accompanied by changes in dynamic response, resulting in a trade-off between accuracy and response speed that has not been extensively analyzed. This study aims to quantitatively evaluate the effect of Moving Average Filter (MAF) parameters on this trade-off in light sensor systems. The proposed method employs a first-order system simulation with a step input signal contaminated by White Gaussian Noise, which is subsequently processed using the MAF with various window sizes ( = 3, 5, 10, 20, and 40). The evaluation is conducted using Root Mean Square Error (RMSE) and rise time as indicators of estimation accuracy and response speed, respectively. The results demonstrate that increasing the window size significantly reduces the RMSE, decreasing from 0.0156 to 0.0072 under step-up conditions and from 0.0132 to 0.0066 under step-down conditions. Optimal performance is observed within the range of = 10–20. However, this improvement in accuracy is accompanied by an increase in rise time, from 0.1027 s to 0.1175 s for step-up conditions and from 0.1036 s to 0.1191 s for step-down conditions, indicating a slower dynamic response. These findings confirm the existence of a trade-off between signal accuracy and response speed. Therefore, this study provides a quantitative basis for determining optimal filter parameters by considering the balance between measurement accuracy and system responsiveness in light intensity sensing applications.
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M. Pamungkas, Hafiddudin, and Y. S. Rohmah, “Perancangan dan Realisasi Alat Pengukur Intensitas Cahaya,” J. ELKOMIKA, vol. 3, no. 2, pp. 120–132, 2015, doi: 10.26760/elkomika.v3i2.120. DOI: https://doi.org/10.26760/elkomika.v3i2.120
Y. Wu, K. Ma, Z. Wu, and W. Zhang, “Intensity Noise Suppression in Photonic Detector Systems for Spectroscopic Applications,” Sensors, vol. 25, pp. 1–14, 2025, doi: 10.3390/s25226932. DOI: https://doi.org/10.3390/s25226932
T. B. Bano et al., “Perancangan Alat Ukur Intensitas Cahaya menggunakan Sensor BH1750 Berbasis Mikrokontroler ATMega328P,” Kappa J., vol. 8, no. 1, pp. 95–101, 2024, doi: 10.29408/kpj.v8i1.24917. DOI: https://doi.org/10.29408/kpj.v8i1.24917
N. Nasution, A. Supriyanto, and S. W. Suciyati, “Implementasi Sensor Fotodioda sebagai Pendeteksi Serapan Sinar Infra Merah pada Kaca,” J. Teor. dan Apl. Fis., vol. 03, no. 02, pp. 111–116, 2015.
N. Shi, J. Yang, Z. Cao, and X. Jin, “A Programmable Ambient Light Sensor with Dark Current Compensation and Wide Dynamic Range,” Sensors, vol. 24, pp. 1–10, 2024, doi: 10.3390/s24113396. DOI: https://doi.org/10.3390/s24113396
J. A. Nava-Pintor et al., “Development and Evaluation of Solar Radiation Sensor Using Cost-Effective Light Sensors and Machine Learning Techniques,” Technologies, pp. 1–31, 2025, doi: 10.3390/technologies13050182. DOI: https://doi.org/10.3390/technologies13050182
Heriansyah and F. B. Gultom, “Evaluasi Kinerja Berbagai Jenis Sensor LDR,” J. Kumparan Fis., vol. 8, no. 3, pp. 87–94, 2025, doi: 10.33369/jkf.8.3.87-94. DOI: https://doi.org/10.33369/jkf.8.3.87-94
A. A. Syukron and I. L. Elviyanti, “Pembuatan Sensor Cahaya dengan Memanfaatkan LED dan LDR Berbasis Arduino Uno,” J. Kridatama Sains dan Teknol., vol. 03, no. 02, pp. 161–169, 2021, doi: 10.53863/kst.v3i02.435. DOI: https://doi.org/10.53863/kst.v3i02.435
A. Fahmi and I. Kurniawan, “Studi Kinerja Transmisi Data Menggunakan ESP32 dan Raspberry Pi Pico Berbasis Simulasi Wokwi,” Zetroem, vol. 07, no. 01, pp. 80–87, 2025, doi: 10.36526/ztr.v7i1.5102. DOI: https://doi.org/10.36526/ztr.v7i1.5102
M. A. Annas, A. Widodo, M. C. Aisiyah, I. E. Ningrum, and D. Makrufah, “Karakterisasi Sensor Cahaya Light Dependent Resistor (LDR),” Pendidik. dan sains, vol. 2, no. 4, pp. 612–622, 2022, doi: 10.58578/masaliq.v2i4.516. DOI: https://doi.org/10.58578/masaliq.v2i4.516
M. F. Lathifah and A. Doyan, “Arduino-Based Light Intensity Measurement,” J. Sci. Technol. Inov., vol. 1, no. 1, pp. 1–3, 2022, doi: 10.56566/amplitudo.v1i1.5. DOI: https://doi.org/10.56566/amplitudo.v1i1.5
C. H. Pardosi, M. Siregar, and L. W. Pandjaitan, “Design and implementation of a dual-axis solar tracking system using Arduino Uno microcontroller,” J. ELTIKOM, vol. 8, no. 1, pp. 44–56, 2024, doi: 10.31961/eltikom.v8i1.1105. DOI: https://doi.org/10.31961/eltikom.v8i1.1105
N. Rinanto, A. P. A. Wuri, R. Y. Adhitya, and H. Ismail, “Comparative Simulation of Kalman Filter and Moving Average on Siemens S7-1200 PLC-Based Loadcell Sensor Readings,” Sustain. Energy Control Optim., vol. 1, no. 2, pp. 67–76, 2025, doi: 10.62762/seco.2025.538779. DOI: https://doi.org/10.62762/SECO.2025.538779
M. M. Ardestani and H. Yan, “Noise Reduction in Human Motion-Captured Signals for Computer Animation based on B-Spline Filtering,” Sensors, vol. 22, pp. 1–15, 2022, doi: 10.3390/ s22124629. DOI: https://doi.org/10.3390/s22124629
A. S. Fiddariani and B. Sumanto, “Studi Penerapan Filter Digital pada Sistem Pemantau Parameter di Kolam Ikan,” J. List. Instrumentasi, dan Elektron. Terap., vol. 5, no. 1, pp. 30–38, 2024, doi: 10.22146/juliet.v5i1.87377. DOI: https://doi.org/10.22146/juliet.v5i1.87377
J. Tong, Y. Zhang, H. Huang, J. Xiong, and Y. Luo, “Composite filter-based detection of rock joints from drilling parameters,” Sci. Rep., vol. 15, pp. 1–16, 2025, doi: 10.1038/s41598-025-26233-4 1. DOI: https://doi.org/10.1038/s41598-025-26233-4
E. P. Rodrigues, D. S. dos Santos, and F. Buiochi, “Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field,” eng, vol. 4, pp. 2582–2600, 2023, doi: 10.3390/eng4040148. DOI: https://doi.org/10.3390/eng4040148
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 Univ. J. Sci., vol. 21, no. 4, pp. 146–159, 2025, doi: 10.18466/cbayarfbe.1682594. DOI: https://doi.org/10.18466/cbayarfbe.1682594
F. Baskoro, M. Rohman, D. Arya, and A. P. Nurdiansyah, “Optimizing Stability of Ultrasonic Sensor Readings : Study on Moving Average Filter Parameter Selection,” Zetroem, vol. 07, no. 01, pp. 59–65, 2025, doi: 10.36526/ztr.v7i1.3603. DOI: https://doi.org/10.36526/ztr.v7i1.3603
M. R. Fauzi, S. Pramono, and G. Wibisono, “Implementasi Moving Average Filter untuk Sensor Arus pada Sistem Pemantauan Penerangan Jalan Umum,” J. Teknol. Rekayasa, vol. 8, no. 1, pp. 1–8, 2023, doi: 10.31544/jtera.v8.i1.2023.1-8. DOI: https://doi.org/10.31544/jtera.v8.i1.2022.1-8
S. Pramono, J. B. Tarihoran, and G. Wibisono, “Implementasi Moving Average Filter untuk Sensor Tegangan Pada Sistem Kontrol Dan Monitoring Lampu Jalan,” Din. Rekayasa, vol. 19, no. 1, pp. 37–44, 2023, doi: 10.20884/1.dr.2023.19.1.536. DOI: https://doi.org/10.20884/1.dr.2023.19.1.536
F. Baskoro, I. G. P. Asto, M. Rohman, D. A. Soeadyfa, and A. P. Nurdiansyah, “Impact of Sample Size Variation on Moving Average Filter Performance for Stability and Accuracy in Ultrasonic Sensor Measurements,” TEM J., vol. 14, no. 2, pp. 1681–1688, 2025, doi: 10.18421/TEM142. DOI: https://doi.org/10.18421/TEM142-65
K. Ogata, Modern Control Engineering, 5th ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010. ISBN: 978-0-13-615673-4.
C. Liu, J. Jiang, J. Jiang, and Z. Zhou, “Enhanced Grid-Connected Phase-Locked Loop Based on a Moving Average Filter,” IEEE Access, vol. 8, pp. 5308–5315, 2020, doi: 10.1109/ACCESS.2019.2963362. DOI: https://doi.org/10.1109/ACCESS.2019.2963362
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