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Meyzia, Bunga
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Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Characteristics of human voice vibrations based on FBG strains Nurpadilla, Rani; Meyzia, Bunga; Saktioto, Saktioto; Fadhali, Mohammed M
Science, Technology and Communication Journal Vol. 4 No. 2 (2024): SINTECHCOM Journal (February 2024)
Publisher : Lembaga Studi Pendidikan and Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v4i2.257

Abstract

FBG is widely developed as a sensor in its application as a sensor, FBG is commonly used either in industry or in clinical applications to measure changes in physical parameters such as pressure, strain, temperature, and corrosion, as well as to monitor the body's heartbeat and breathing. This research uses 2 types of FBG, namely uniform and chirping. The spectrum used is in the range of 1550 nm. Using an optical sensing interrogator as a tool to read wavelength changes as well as input and output with an infrared laser light source. This study aims to analyze the response of FBG sensors to human voice vibrations with variations in the intensity of sound violence. The results showed that at a hardness intensity of 60 dB using a uniform FBG with a reflectivity of 10% experienced a wavelength change of -0.0304 nm, at an intensity of 70 dB 0.0304 nm, and an intensity of 80 dB experienced many wavelength changes 0.06669 nm. The greater the intensity of the sound, the more FBG response shows an increase in wavelength. The largest strain value detected by the uniform FBG with 10% reflectivity is at 70 dB intensity of 5.5579 × 10-5 strain while the lowest value is at 80 dB intensity of 4.4816 × 10-5 strain. The chirping FBG with 10% reflectivity has the highest strain value at 70 dB intensity with a respective strain value of 1.18 × 10-4 strain. Giving sound vibrations such as some of A, I, U, E, and O to FBG is useful for calculating how the transmission peak of FBG shifts due to strain. When the object emits sound vibrations with a certain intensity, the pressure that occurs will be more than the object when it is at rest, so the greater the sound vibration, the greater the strain that occurs.
Novel approach peak tracking method for FBG: Gaussian polynomial technique Meyzia, Bunga; Emrinaldi, Tengku; Wanara, Nadiah; Hanto, Dwi; Widyatmoko, Bambang; Rianaris, Agitta; Syahadi, Mohamad; Hairi, Haryana Mohd
Science, Technology and Communication Journal Vol. 4 No. 3 (2024): SINTECHCOM Journal (June 2024)
Publisher : Lembaga Studi Pendidikan and Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v4i3.262

Abstract

This paper presents a novel approach for tracking the peaks in the FBG spectrum using the Gaussian polynomial method. The proposed algorithm involves preprocessing the FBG signal, detecting the peaks, and fitting the peaks with a Gaussian function. The performance of the algorithm is evaluated using both simulated and experimental FBG spectra. This method involves fitting a Gaussian function to the peak of interest and using the fitted parameters to estimate peak height, width, and location. The method is highly accurate and precise and can provide detailed information about peak shape and position, making it effective for tracking complex or overlapping peaks. However, the method can be computationally intensive and may require careful selection of initial parameters to ensure accurate results. Despite these limitations, the Gaussian polynomial method is a powerful tool for peak tracking and analysis in various application.
Determination of optical parameters on knee bending of the feet using fiber optic Fitri, Ade; Candra, Wahyu; Meyzia, Bunga
Science, Technology and Communication Journal Vol. 2 No. 1 (2021): SINTECHCOM Journal (October 2021)
Publisher : Lembaga Studi Pendidikan and Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v2i1.203

Abstract

Fiber optics are the right transmission medium to monitor the movement of the human body, one of them is knee activity. Single-mode fiber (SMF) and fiber Bragg grating (FBG) as sensing sensors that can monitor damage to bad conditions of the human joint area so can prevent further damage. The purpose of this study was to design fiber optic-based belts due to macrobending with sinusoidal patterns, determine the relationship of knee benders with average power change of SMF and FBG, measure changes in power loss (L) fiber optics as a function of optical fiber diameter (d) and knee angle, and determine the optimum sensitivity (S) of SMF and FBG in detecting knee bending. The results showed that L of 0.0751 dB and S of 0.017442 dB are the largest values produced from the SMF belt with values d of 12 mm and knee angle of 180°. The values L of 2.0177 dB and S of 0.591382 dB are the largest values produced from FBG belts with values d of 8 mm and knee angle of 180°. The results of this study explain that FBG is more effective to use because it has a higher S value than SMF.
Electrocardiogram signal patterns detection of myocardial ischemia rhythm using an artificial neural network based on MATLAB/Simulink Arianto, Yendra; Hamdi, Muhammad; Meyzia, Bunga
Science, Technology and Communication Journal Vol. 3 No. 1 (2022): SINTECHCOM Journal (October 2022)
Publisher : Lembaga Studi Pendidikan and 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 Arianto, Yendra Candra, Wahyu Dwi Hanto Fadhali, Mohammed M Fitri, Ade Hairi, Haryana Mohd Hamdi, Muhammad Mohamad Syahadi Nurpadilla, Rani Rianaris, Agitta Saktioto Saktioto Tengku Emrinaldi Wanara, Nadiah Widyatmoko, Bambang