DOI: https://doi.org/10.14710/medstat.17.2.173-184

UNCERTAINTY ANALYSIS OF VOLTAGE MEASUREMENT USING ATMEGA328P MICROCONTROLLER: AN ANOVA TEST APPROACH

*James Julian orcid scopus publons  -  Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Ade Fikri Fauzi  -  Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Annastya Bagas Dewantara  -  Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Faiz Daffa Ulhaq  -  Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Fitri Wahyuni  -  Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The voltage sensors are widely used in various applications. In certain applications, such as medical devices, autonomous vehicles, or the military, the sensor's accuracy and level of precision play an important role, making it necessary to evaluate the sensor's performance. In this research, testing of direct current (DC) voltage sensors was carried out using analysis of variance (ANOVA) and Tukey honestly significant difference (HSD) to test sensor performance in various voltage ranges. This research used an experimental-based quantitative approach, using the ATmega328P. Data collection begins by calibrating the analog-to-digital converter (ADC) readings against voltage values with linear regression, the Chauvenet criterion to eliminate outlier data caused by noise from the environment, One-way ANOVA is used to determine differences in variations between voltage distances, and a Q-Q plot is used to determine the normality of the sensor error distribution. This research obtained from Tukey-HSD that 9 comparisons accepting the null hypothesis (H0). And 27 pairs accepting the alternate hypothesis (H1). The data was found to be normally distributed through the calculation of residual ANOVA, and visualization of data with the Q-Q plot, and the use of the sensor was effective in the range of 3V to 24.5V.
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Keywords: Voltage sensor; ATmega328p; linear regression; Chauvenet; Tukey-HSD.

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Language : EN
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  7. Hrbac, R., Kolar, V., Bartlomiejczyk, M., Mlcak, T., Orsag, P., & Vanc, J. (2020). A Development Of A Capacitive Voltage Divider For High Voltage Measurement As Part Of A Combined Current And Voltage Sensor. Elektronika Ir Elektrotechnika, 26(4), 25–31
  8. Junaldy, M., Sompie, S. R., & Patras, L. S. (2019). Rancang Bangun Alat Pemantau Arus dan Tegangan di Sistem Panel Surya Berbasis Arduino. Jurnal Teknik Elektro dan Komputer, 8(1), 9–14
  9. Lascano, A. M., Lalive, P. H., Hardmeier, M., Fuhr, P., & Seeck, M. (2017). Clinical Evoked Potentials in Neurology: A Review of Techniques and Indications. Journal of Neurology, Neurosurgery and Psychiatry, 88(8), 688–696
  10. Maria, A., Suhartanto, E., & Fidari, J. S. (2022). Validasi Data Curah Hujan Satelit dengan Data Stasiun Hujan di DAS Sadar, Kabupaten Mojokerto. Jurnal Teknologi dan Rekayasa Sumber Daya Air, 2(2), 367–375
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  13. Tancev, G., & Toro, F. G. (2022). Variational Bayesian Calibration of Low-Cost Gas Sensor Systems in Air Quality Monitoring. Measurement: Sensors, 19, 100365
  14. Van Der Veen, A. M. H. (2018). Bayesian Methods for Type A Evaluation of Standard Uncertainty. Metrologia, 55(5), 670–684
  15. Wang, C., Caja, J., & Gómez, E. (2018). Comparison of Methods for Outlier Identification in Surface Characterization. Measurement, 117, 312–325
  16. Zhang, C. G., Chen, C. J., Settu, K., & Liu, J. T. (2018). Angle-Scanning Surface Plasmon Resonance System with 3D Printed Components for Biorecognition Investigation. Advances in Condensed Matter Physics, 2018, 1–7

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