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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Makara Journal of Technology
Muhammad Febrian Rachmadi, Muhammad Febrian
Unknown Affiliation
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Automatic Arrhythmia Beat Detection: Algorithm, System, and Implementation Jatmiko, Wisnu; Setiawan, I Md. Agus; Akbar, Muhammad Ali; Suryana, Muhammad Eka; Wardhana, Yulistiyan; Rachmadi, Muhammad Febrian
Makara Journal of Technology Vol. 20, No. 2
Publisher : UI Scholars Hub

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Abstract

Cardiac disease is one of the major causes of death in the world. Early diagnose of the symptoms depends on abnormality on heart beat pattern, known as Arrhythmia. A novel fuzzy neuro generalized learning vector quantization for automatic Arrhythmia heart beat classification is proposed. The algorithm is an extension from the GLVQ algorithm that employs a fuzzy logic concept as the discriminant function in order to develop a robust algorithm and improve the classification performance. The algorithm is tested against MIT-BIH arrhythmia database to measure the performance. Based on the experiment result, FN-GLVQ is able to increase the accuracy of GLVQ by a soft margin. As we intend to build a device with automated Arrhythmia detection, FN-GLVQ is then implemented into Field Gate Programmable Array to prototype the system into a real device.
Astronomical Image Denoising Using AttentionGAN Naufal, Faishal Zaka; Rachmadi, Muhammad Febrian; Krisnadhi, Adila Alfa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i1.13154

Abstract

Denoising astronomical images is a significant challenge in the field of astronomical data processing. Image data acquired from astronomical sources typically contains noise from various sources. The study aims to investigate the denoising of astronomical images using an image-to-image translation approach with AttentionGAN method. This method combines attention-guided techniques with a Generative Adversarial Network (GAN) model to improve the quality of noisy astronomical images. Attention-guided technique allows the model to learn the most important features of the image and guide the image generation process. This approach has been tested on several images in different domains, each with varying levels of noise. The results shows that AttentionGAN method produces denoised images with better and sharper quality than several other denoising methods. Two databases, The Panoramic Survey Telescope and Rapid Response System (PAN-STARRS) and the Sloan Digital Sky Survey (SDSS), were used in this research. Images acquired from PAN-STARRS contain noise, while images acquired from SDSS are clean. Overall, this research contributes to improving the quality of astronomical images by demonstrating the effectiveness of the AttentionGAN method in denoising noisy astronomical images. We employed denoising techniques using CycleGAN and AttentionGAN and evaluated them using metrics such as PSNR, SSIM, and FID. The analysis showed that the AttentionGAN model outperformed CycleGAN. We also conducted ablation studies to further investigate the components of the AttentionGAN model. This study provides a foundation for future research in the field of astronomical data processing, which has the potential to enhance image quality.
Co-Authors Krisnadhi, Adila Alfa Muhammad Ali Akbar, Muhammad Ali Naufal, Faishal Zaka Setiawan, I Md. Agus Suryana, Muhammad Eka Wardhana, Yulistiyan Wisnu Jatmiko