A Novel Subtraction Method for Signal Fluctuation

Fluctuations in signal processing are a state of inaccuracy in the peak amplitude value of data during the data acquisition process. There are three types of fluctuation data that have been obtained in previous studies, namely Mean Fluctuation (MF), High Fluctuation (HF), and High-High Fluctuation (HHF). This study only used High Fluctuation (HF) signal data from H20 and H20 mixed with HCl. The method in this research is in the form of spectral subtraction, which is done by reducing the average data group for signal fluctuations of H2O with H2O mixed with HCl due to multispectral capacitive sensors. Then the data groups obtained before and after the spectral subtraction process were analyzed and compared using signal quality parameters to see which data group had the best fluctuation pattern. In addition, this study also shows the fluctuation of the material, which is more dominant through the spectral subtraction process. From the research that have been done, the data group per 75 data shows the best SNR value because this group uses a lot of input data compared to other groups. This data group can also be used as a reference for further research into one of the data grouping techniques.