DOI: https://doi.org/10.14710/jtsiskom.2020.13868

Analisis penerapan tapis Wiener pada segmentasi pola fluktuasi spektral

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

Department of Electrical and Computer Engineering, Universitas Syiah Kuala. Jl. Tgk Syech Abdurrauf No.7, Darussalam, Banda Aceh 23111, Indonesia

Received: 18 Aug 2020; Revised: 20 Nov 2020; Accepted: 27 Nov 2020; Available online: 7 Dec 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The Wiener filter is an adaptive filter which able to produce the desired estimates. Besides, this filter can also suppress noise in digital signal processing. This study aims to segment the fluctuation pattern, which results from data acquisition from a capacitive sensor with the object H2O. The fluctuation pattern to be processed is the High Fluctuation (HF) pattern by dividing the pattern into several segments according to the input frequency. It aims to see in more detail and clearly the state of each segmentation of the pattern. The results will show noise attenuation and suppression after filtering with a Wiener filter. The Signal to Noise Ratio (SNR) value will also be analyzed, which shows that the signal quality is getting better after applying the Wiener filter. Then, the analysis of the Mean Square Error (MSE) results can provide more consistent results with a smaller average error.
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Keywords: multi-spectral; flutuation; noise; Wiener filter; adaptive filter
Funding: Universitas Syiah Kuala under contract No. 118/UN11.2.1/PT.01.03/PNBP/2020.

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Section: Original Research Articles
Language : ID
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  1. M. Melinda, A. S. Tamsir, B. Basari, and D. Gunawan, “Performance of consistency parameters analysis using fourier and wavelet transform on multi spectral fluctuation signal‖,” in International Conference on Electrical Engineering and Informations, Banda Aceh, Indonesia, Oct. 2017, pp. 7-11. doi: 10.1109/ICELTICS.2017.8253248
  2. Y. Shouqi, N. I. Yongyan, P. A. N. Zhongyong, and Y. Jianping, “Unsteady turbulent simulation and pressure fluctuation analysis for centrifugal pumps,” Chinese Journal of Mechanical Engineering, vol. 22, no. 1, pp. 64–69, 2009. doi: 10.3901/CJME.2009.01.064
  3. H. Pan and M. Bu, “Pressure fluctuation signal analysis of pump based on ensemble empirical mode decomposition method,” Water Science and Engineering, vol. 7, no. 2, pp. 227–235, 2014
  4. M. Melinda and A. S. Tamsir, “Comparative analysis of material fluctuation response based on data set groups,” in International Conference on Electrical Engineering and Informatics October 2017, Banda Aceh, Indonesia, Oct. 2017, pp. 263-268
  5. M. Melinda, A. S. Tamsir, Basari, and D. Gunawan, “Analysis of consistence level using new method of statistical transformation approach in multi spectral fluctuation pattern,” in 6th IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, Nov. 2016, pp. 251-255. doi: 10.1109/ICCSCE.2016.7893580
  6. M. Melinda, A. S. Tamsir, D. Sudiana, D. Gunawan, and M. Iqbal, “Implementation of segmentation scheme based on wavelet transform in multi-spectral fluctuation patterns,” International Journal of Telecommunication, Electronic and Computer Engineering, vol. 08 no.12, pp. 47-52, 2016
  7. M. Melinda, A. S. Tamsir, B. Basari, P. Mahatidana, and D. Gunawan, “The influence of wideband amplifier supply on the consistency level of multi-spectral fluctuation pattern,” Advanced Science Letter, vol. 23, no. 4, pp.2637-3816, 2017. doi: 10.1166/asl.2017.9037
  8. M. Melinda, A. Tanjung, A. S. Tamsir, B. Basari, and D. Gunawan, “Grouped data analysis of H2O and H2O mixed with NaOH on multi spectral High fluctuation pattern,” in International Conference on Electrical Engineering and Informatics, Banda Aceh, Indonesia, Oct. 2017, pp.184-188. doi: 10.1109/ICELTICS.2017.8253266
  9. S. P. D. Sriyanto and R. M. Sipayung, “Implementasi pengurangan noise seismik menggunakan filter Wiener pada algoritma deteksi otomatis sinyal gempabumi,” thesis, Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Tangerang, Indonesia, 2017
  10. Y. Wang and T. Li, “Application of wavelet and Wiener filtering algorithm in image de-noising,” Open Access Library Journal, vol. 3, e2319, 2016. doi: 10.4236/oalib.1102319
  11. J. Chen, J. Benesty, Y. A. Huang, and S. Doclo, “New insights into the noise reduction Wiener filter,” IEEE Transactions on Audio, Speech, And Language Processing, vol. 14, no. 4, pp. 1218-1234, 2006. doi: 10.1109/TSA.2005.860851
  12. S. G. Bacheramsyah, M. S. Suraatmadja, and B. D. Kuncoro, “Perancang pengolah sinyal ekg dengan menggunakan filter Wiener pada software Labview secara real time,” thesis, Universitas Telkom, Bandung, Indonesia, 2015
  13. D. S. Nurjanah, D. Suhaedi, and E. Harahap, “Denoising restorasi citra digital menggunakan filter Wiener,” Jurnal Matematika UNISBA, vol. 15, no. 1, pp. 1-6, 2016
  14. E. Anderes, “Robust adaptive Wiener filtering,” in IEEE International Conference on Image Processing, Orlando, USA, Sep. 2012, pp. 3081–3084. doi: 10.1109/ICIP.2012.6467551
  15. X. Yousheng and H. Jianwen, ”Speech enhancement based on combination of Wiener filter and subspace filter,“ in International Conference on Audio, Language and Image Processing, Shanghai, China, Jul. 2014, pp. 459-463. doi: 10.1109/ICALIP.2014.7009836
  16. S. Yadav, B. A. Khrisna, and M. Kamaraju, “Performance of Wiener filter and adaptive filter for noise cancellation in real-time environment,” International Journal of Computer Applications, vol. 97, no. 15, pp. 16-23, 2014. doi: 10.5120/17084-7536
  17. A. Purwadi, “Skabilitas signal to noise ratio pada pengkodean video dengan derau gaussian,” Jurnal Rekayasa Elektrika, vol. 11, no. 3, pp. 79-85, 2015. doi: 10.17529/jre.v11i3.2243

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