Simulasi Perbandingan Filter Savitzky Golay dan Filter Low Pass Butterworth pada Orde Ketiga Sebagai Pembatal Kebisingan

Abstract

Nowadays, the Information and Communication Technology (ICT) is rapidly developed. It also trigs the development of other research field such as social science research. But in the development of it, there are a continues problem that has been discovered over 30 years, noise. Over the years, many ways have been created for example Savitzky – Golay (SG) and Low Pass (LP) Butterworth filter. In order to use SG filter, two parameters which are the order and the window length should be determined by trial and error. On the other hand, LP Butterworth filter also needs two parameters to be operated which are the order and the cut off frequency. This research focuses on comparing the performance of third order LP Butterworth filter and third order SG filter by finding the gap between filtered signal and the original signal through the simulation by using MATLAB. This research is supported by the journals and books references. Also, the data of this research is presented by the table and graph. According to this research, founded that both filters have a significant impact to smoothing the noisy signal. compare to LP Butterworth filter, SG filter has better performance. It is proven by SG filter only has 5% gap to the original signal where LP Butterworth filter has a slightly bigger gap, 8.82%.

Integrated LSPR Biosensing Signal Processing Strategy and Visualization Implementation

“…Numerous researchers optimize the data processing methods for LSPR peak wavelength through several key strategies: the first is advanced spectral analysis algorithms, such as polynomial curve fitting [4,5], wavelet transforms [6,7], fixed baseline algorithm [8], dynamic baseline algorithm [9], constant algorithm [10], and so on. The second is adaptive data filtering, such as Savitzky-Golay polynomial filter [11,12], wavelet transform [13], singular value decomposition [14], morphological filtering [15], and so on. The third is real-time monitoring and feedback [4], which enables the dynamic adjustment of data processing parameters, ensuring accurate determination of LSPR spectral resonant peak wavelengths under varying conditions.…”