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All Journal JFA (Jurnal Fisika dan Aplikasinya) Journal of Natural Sciences and Mathematics Research Knowledge Engineering and Data Science Physics Education Research Journal Jurnal Fisika dan Terapannya Parta: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan
Heni Sumarti
Universitas Islam Negeri Walisongo Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Other

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Journal : Journal of Natural Sciences and Mathematics Research

 1 
Analysis of chest X-Ray (CXR) images in COVID-19 patients based on age using the Otsu thresholding segmentation method Uhty Maesyaroh; Laelatul Munawaroh; Heni Sumarti; Rico Adrial
Journal of Natural Sciences and Mathematics Research Vol 7, No 2 (2021): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.2021.7.2.10891

Abstract

The infection with the COVID-19 virus or better known as the Corona virus spread throughout China and other countries around the world until it was designated a pandemic by the World Health Organization (WHO). Detection of patients infected with COVID-19 in the form of RT-PCR, CT-Scan images and Chest X-Ray (CXR). This study aims to analyze CXR images of COVID-19 patients based on age using Otsu Thresholding Segmentation. The image segmentation process uses the Otsu auto-tresholding method to separate objects from the background on the CXR image. The results show that the images of COVID-19 patients have pneumonia spots that are not visible on the original CXR image. The average value of the accuracy of the Otsu Thresholding results is 95.18%. Penunomia spots are mostly found in COVID-19 patients aged 50 to 70 years and over which cause severe lung damage.©2021 JNSMR UIN Walisongo. All rights reserved.
Classification of Pneumonia in Thoracic X-Ray images based on texture characteristics using the MLP (Multi-Layer Perceptron) method Latifatul Istianah; Heni Sumarti
Journal of Natural Sciences and Mathematics Research Vol 6, No 2 (2020): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.2020.6.2.11228

Abstract

One of the diseases that attack the lungs is pneumonia. This disease can attack someone with a weak immune system. Pneumonia is inflammation of the lungs that can be caused by pathogens, such as bacteria, viruses, and fungi. The purpose of this study was to classify fungal pneumonia, bacterial pneumonia, and lipoid pneumonia based on texture characteristics and the MLP method using machine learning WEKA. The method in this study has three stages including pre-processing, extraction of texture features consisting of Histogram and GLCM, and classification using the MLP (Multi Layer Perceptron) method. The results of the texture feature extraction showed that the three types of pneumonia were: lipoid pneumonia with brightness, sharp contrast random distribution on correlation characteristics, bacterial pneumonia with high brightness, high contrast random distribution on energy characteristics, and fungal pneumonia with brightness, sharp contrast, random distribution of homogeneity attributes. The third similarity of pneumonia is in the gray level that collects each other in the middle. The method used in this study resulted in the same accuracy, sensitivity, and specificity, namely 100%. The image classification in this study shows the success of the texture features and the MLP method in classifying pneumonia images accurately so that they can be used as additional tools that make it easier for medical experts.   ©2020 JNSMR UIN Walisongo. All rights reserved. 
Analysis of Axial CT-Scan image of COVID-19 patients based in gender using the Otsu Thresholding method Melany Puspa Damayanti; Heni Sumarti
Journal of Natural Sciences and Mathematics Research Vol 6, No 1 (2020): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.2020.6.1.11152

Abstract

At the beginning of 2020, the world was shocked by the emergence of the COVID-19 virus. This virus has spread to all corners of the world, not only in Indonesia. Therefore, the government needs to make efforts to break the chain of transmission of this virus. One of these efforts is to detect COVID-19 as early as possible. Using CT images can be one of the early detection efforts of early-phase lung infections in COVID-19 patients. The stage in detecting COVID-19 is by segmenting the image. In this study, segmentation was carried out using the Otsu Thresholding method on 8 axial CT images of the lungs of COVID-19 patients, consisting of 4 images of male patients and 4 images of female patients. Then the image segmentation results of male and female patients were compared and evaluated using ROC measurements, Threshold (T) values and analyzed for GGO (grand-glass opacity). The result can be seen that judging from the value of the ROC measurement results, the measurement of image segmentation evaluation of male patients is more accurate than female patients. The number of false negatives for male patients and female patients is the same, while the number of false positives for male patients is less than female patients. Threshold value of the image segmentation results of male and female patients is the same so that the density of image segmentation is the same. GGO (grand-glass opacity) for male COVID-19 patients aged between 45-55 years is fuller than female COVID-19 patients aged 45-55 years. This shows that men are more at risk of dying from COVID-19 than female.©2020 JNSMR UIN Walisongo. All rights reserved.
Co-Authors Affa Ardhi Saputri Agus Sudarmanto Alvania Nabila Tasyakuranti Alvnia Nabila Tasyakuranti Fachrizal Rian Pratama Fahira Septiani Farah Alfiana Na’ila Hamdan Hadi Kusuma Hartono Hartono Irman Said Prastyo Istikomah Istikomah Laelatul Munawaroh Lailiyatu Latifah Latifatul Istianah Melany Puspa Damayanti Muhammad Labib Qisthi Fariyani Rico Adrial Rizki Edmi Edison Sheilla Rully Anggita Sosiawati Teke Susilawati Susilawati Tria Nurmar’atin Uhty Maesyaroh Wahyu Caesarendra