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All Journal Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) International Journal of Technology and Education Research
Diah Rahayu Ningtias
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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Classification of Pneumonia Disease Based on Web Application Using Convolutional Neural Networks (CNNs) Tri Suhartono; Diah Rahayu Ningtias
International Journal of Technology and Education Research Vol. 1 No. 01 (2023): January - March : International Journal of Technology and Education Research (
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v1i01.267

Abstract

Artificial Intelligence can be used as a medical image diagnosis model by training a deep learning Convolutional Neural Networks (CNNs) image classification model to classify X-Ray images of the lungs. In this study, X-Ray images of the lungs classified as pneumonia or normal were used by the CNNs model that had been built. In the process of diagnosis there are difficulties, one of which is the lack of predictability of a follow up examination procedure only through X-Ray image. We need a method that can help diagnose X-Ray image objectively, quantitatively, and has high accuracy. There are used the CNNs model is then applied to Streamlit web-based application systems. The aim of this research is to develop a pneumonia disease classification application to simplify the diagnosis process. In this study, 5,855 X-Ray images of the lungs were used. The model is given epoch values ​​of 10, 15 and 20 with 326 steps per epoch and the highest accuracy value is obtained at epoch 20. The result of accuracy value is 91.504%. The accuracy value can be affected from the epoch value, but the addition of the epoch value cannot fully increase the resulting accuracy value. The CNNs program model that has been created is then deployed to a web application using Streamlit and can be used for X-Ray image classification. The results of using this web application can be used for the initial detection process of X-Ray images with a diagnosis of pneumonia.
Kalibrasi CT Scan Merk Siemens Type Somatom go.Top di RSUD Banyumas Agustinus Lise; Diah Rahayu Ningtias; Imam Tri Harsoyo
JURAL RISET RUMPUN ILMU TEKNIK Vol. 3 No. 2 (2024): Oktober : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v3i2.7469

Abstract

CT Scan calibration is necessary to ensure the accuracy, consistency, and reliability of the medical imaging results used in the diagnosis process. This study aims to find out the calibration procedure and analyze the data of the calibration results of the Siemens SOMATOM go CT Scan. TOP at Banyumas Hospital. The test method refers to the Ministry of Health's CT Scan Testing Working Method No. MK: 056-18, which is the standard in evaluating the performance of CT Scan equipment. The parameters tested included the percentage of tube voltage error (kVp) and the quality of the X-ray beam represented by the Half Value Layer (HVL) value. The results of the measurement of the error percentage at various kVp points show a range of 0.37–0.88%, which is still within the tolerance limit set by the national standard. In addition, the results of the HVL test at voltages of 120 kVp and 140 kVp also showed values that were above the minimum required limit, so that the quality of the X-ray beam was considered to meet the requirements. Based on the overall test and analysis results, the Siemens SOMATOM CT Scan go. TOP is declared feasible, safe, and good to use because all performance parameters of the tool have met applicable standards.
Co-Authors Agustinus Lise Imam Tri Harsoyo Tri Suhartono