Minimizing Noise in Sinusiodal Function Signal using Wavelet Transform
Irpan Hidayat1, Made Suangga2, Roesdiman Soegiarso3, Putri Arumsari4, Yuliastuti5
1Irpan Hidayat*, Civil Engineering Department, Bina Nusantara University, Jakarta, Indonesia.
2Made Suangga, Civil Engineering Department, Bina Nusantara University, Jakarta, Indonesia.
3Roesdiman Soegiarso, Civil Engineering Department, Tarumanagara University, Jakarta, Indonesia.
4Putri Arumsari, Civil Engineering Department, Bina Nusantara University, Jakarta, Indonesia.
5Yuliastuti, Civil Engineering Department, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1254-1260 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2824129219/2020©BEIESP | DOI: 10.35940/ijeat.B2824.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Resistances that occur in retrieving and processing signal is caused by the interference (noise) on the data signal measurement results. The resistance will raise uncertainties in determining the value of the frequency. This is due to the signal which is mixed with the noise in the original signal. In general, the process of signal analysis uses Fast Fourier Transformation (FFT). However, by using FFT in analyzing and reconstructing there are still doubts in determining the real frequency due to the still visible noise in the signal. In this study the signal function used is a sinusiodal function, Y = 2 sinπf1 t + 2 sin πf2 t, with a given noise value of 2 DB. The specified frequency value of f1 and f2 equal to 0.25 Hz and 5 Hz, respectively. This research proposed wavelet transforms to analyze and in filtering original signal with noise. By using the transformation wavelet, signal with noise filtered with the high pass and low pass filter method and also using the Haar wavelet function in analyzing. Once the signal is decomposed using wavelet transformation, the wavelet coefficients value will be obtained. The wavelet coefficient values will then threshold within a range of 5-50%. The purposed in determining the treshold value is to reduce the signal data identified as a noise signal data. If the value of wavelet coefficient below the treshold percentage value multiplied by the maximum wavelet coefficient, it is identified as a noise signal data, and the value of coefficient wavelet will be zero. The wavelet coefficient will then be reconstructed in order to obtain the data signal with the new sinusoidal function. In determining the value of the reconstructed frequency signal, the Fast Fourier Transform (FTT) method is used. The results of the study is signals with noise can be analyzed and filtered using wavelet transforms, by changing the signal into wavelet coefficients. Furthermore, the threshold of 5% is capable in reducing of noise in signal so that the graph of frequency and amplitude showed a clearer value of frequency.
Keywords: Signal, noise, Wavelet transformation.