Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

Prasetyowati, Sri Arttini Dwi; Susanto, Adhi; Widihastuti, Ida

doi:10.11591/eecsi.v3.1125

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B Adaptive Least Mean Square (Lms) Algorithm1

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2022

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“…The values of d and x are target and input, respectively. Both are the same data but input x was a target that was delayed by one sample [4] 4) and (5) the following [12]:…”

Section: B Adaptive Least Mean Square (Lms) Algorithmmentioning

confidence: 99%

Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction

Prasetyowati

Ismail²,

Badieah³

2022

Jurnal Informatika

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This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.

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“…The values of d and x are target and input, respectively. Both are the same data but input x was a target that was delayed by one sample [4] 4) and (5) the following [12]:…”

Section: B Adaptive Least Mean Square (Lms) Algorithmmentioning

confidence: 99%