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All Journal Indonesian Journal of Electronics and Instrumentation Systems
Agfianto Eko Putra
Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM
Electrical & Electronics Engineering

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Analisis Perkiraan TDOA menggunakan Algoritma LMS Adaptif pada Pelacakan Paus Lodan Andriyan Permana; Agfianto Eko Putra; Catur Atmaji
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 5, No 1 (2015): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (775.479 KB) | DOI: 10.22146/ijeis.7149

Abstract

Research on TDOA estimation in sperm whale tracking has been done [1] that uses the Teager - Kaiser operator in improving accuracy the TDOA estimation using adaptive LMS algorithm on sperm whale tracking. However, further researches on the right convergence factor (μ) (fast and accurate) in TDOA estimation using adaptive LMS algorithms for sperm whale tracking have not been performed. Will be analyzed the TDOA estimation using adaptive LMS algorithm in sperm whale tracking with the variation of μ. As a reference to determine the right value of μ, the results of sperm whale tracking[2] is used.TDOA estimation using Adaptive LMS algorithm was first published by Benesty [3]. The approach taken is based on estimation of the impulse responses from source to receivers. In the adaptive LMS algorithm function updates the coefficient with μ as adaptation step. TDOA values obtained from the difference between the two impulse responses.From the results, values changing of μ affect the speed of TDOA estimation using the LMS adaptive algorithm. However, the wrong value of μ is lead to inaccurate sperm whale tracking results. The best value of μ for TDOA estimation using the LMS adaptive algorithm for sperm whale tracking is 0.01.
Pemodelan Generator Uap Berbasis Jaringan Saraf Tiruan dengan Algoritme Pelatihan BPGD-ALAM Fadhlia Annisa; Agfianto Eko Putra
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 6, No 1 (2016): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (969.259 KB) | DOI: 10.22146/ijeis.10766

Abstract

Steam generator is unit plant which has nonlinear and complex system with multiple-input-multiple-output (MIMO) configuration which is hard to be modeled. Whereas, steam generator model is very useful to create simulation such as operator training simulator (OTS). The purpose of this research is to obtain model of steam generator which has 8 output parameters and 9 input parameters based neural network (NN) with BPGD-ALAM training algorithm. Data had been taken from steam generator of PT. Chevron Pacific Indonesia, Duri and it is divided into three types, i.e training data, validation data and testing data. Training data was used to obtain model for each ouput through training process. Verification model is also done for each epoch using validation data to monitor training process whether overfitting occurs or not. Eight NN model of each output which is obtained from training and verification, is tested using testing data for getting its performance. From the reseach results, architecture of neural network models are obtained with various configuration for each output with RMSE value under 9.71 %. It shows that model which has been obtained, close with steam generator real system.
Co-Authors Andriyan Permana Catur Atmaji Fadhlia Annisa