Wavelet Transformation Approach to Identify Several Fluctuation Patterns by Applying The Amplitude Representative Value Scheme

Our research is a development study of data grouping analysis based on the value of representative amplitude (ARV) with the implementation of the FFT transformation. Then, we use MSCS (Multi-Spectral Capacitive Sensor) to facilitate the data acquisition process. Furthermore, in this study, we also compared three research objects: H2O, H2O mixed with NaOH, and H2O mixed HCl. Here, we propose a comparative analysis of ARVs using the Fourier transform in previous research with the wavelet transform method that we recommend. Preliminary research data using Fast Fourier Transform (FFT) has produced 3 (three) fluctuation patterns for each material, namely: MF (Mean Fluctuation), HF (High Fluctuation), and HHF (High High Fluctuation). However, in this study, we only used HF and HHF patterns. The next step we are working on is applying the data grouping method to the ARV approach close to the data processing stage. Every research object that we use will get every ARV value for each fluctuation pattern. Next, in the analysis phase, we compare two fluctuation models (HF and HHF) that apply Fourier and Wavelet transformations for several data sets. In the end, we hope that the results we get can be a reference whose changes have better ARV values to analyze fluctuation patterns to facilitate the process of identifying material characteristics later.