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p-Index From 2021 - 2026
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Muhammad Hamdi
Department of Physics, Universitas Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Analysis of non-destructive testing ultrasonic signal for detection of defective materials based on the Simulink Matlab Mathematica computation method Ade Febrianti; Muhammad Hamdi; Juandi Muhammad
Science, Technology, and Communication Journal Vol. 1 No. 2 (2021): SINTECHCOM Journal (February 2021)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v1i2.27

Abstract

In this paper, an ultrasonic non-destructive test (NDT-UT) has been carried out on steel using the Simulink-MATLAB Mathematica computation method. This study aims to analyze the NDT-UT output signal for material defect detection using secondary data as the first sample. The sample is then analyzed using the fast Fourier transform (FFT) method to produce a spectrum waveform and image thermography. It can be seen that there is a decrease in signal height from 1 a.u to 0.55 a.u. The first sample waveforms were used to analyze the second sample, third sample, and fourth sample, and all the samples had different defects. The results of the sample analysis are in the form of a thermographic image that shows the temperature level based on the distribution of red (R), green (G), and blue (B) images on the sample surface. The NDT-UT output signal produces a sinusoidal wave similar to the results of the Simulink-MATLAB modeling analysis on the initial input echoes and the back wall, with a percentage inequality of 10%. Then validated the sinusoidal signal from the NDT-UT which gave a percentage of inequality between 0% – 42%. More complex or irregular defects result in a larger percentage or vice versa.
Electrocardiogram signal patterns detection of myocardial ischemia rhythm using an artificial neural network based on MATLAB/Simulink Yendra Arianto; Muhammad Hamdi; Bunga Meyzia
Science, Technology, and Communication Journal Vol. 3 No. 1 (2022): SINTECHCOM Journal (October 2022)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v3i1.222

Abstract

This study aims to design a computer program to detect myocardial ischemic heart defects through electrocardiogram (ECG) signal patterns and their accuracy. Myocardial ischemia is a heart disorder caused by the narrowing of the blood vessels in the walls of the heart. The method used is a backpropagation-based artificial neural network (ANN) based on MATLAB/Simulink. The input data is trained to recognize the target pattern of the ECG signal based on the potential and time in the ST segment. The optimal weight of the results of the ANN backpropagation algorithm is used in the process of testing the ECG signal pattern to obtain the ANN output. The ANN output was analyzed for potential depression or elevation to identify normal heart or myocardial ischemia. The results of the training show that from several architectures that have been tested, the optimal ANN architecture is 1 hidden layer with 11 hidden units. These results are obtained from the epoch parameter and the mean square error (MSE) value as well as the accuracy of each architecture. The backpropagation ANN learning process requires 8 epochs to achieve the performance goal with MSE 4.03 × 10-9. The system can recognize target patterns with a training accuracy of 99.82%. The test results of the ANN program identification system can detect myocardial ischemia and normal heart abnormalities with an accuracy of 86.7%. Some data were not detected because the ANN output did not meet the criteria for cardiac ischemia or normal myocardium on the ECG signal. Based on the accuracy of the ANN program identification system, the detection of myocardial ischemia rhythm ECG signal patterns using ANN can be said to work well.
Co-Authors Ade Febrianti Bunga Meyzia Dewi Kurnia Hewa Yaseen Abdullah Juandi Muhammad Preecha Yupapin Saktioto Saktioto Yendra Arianto