dc.contributor.advisor | Herdiyeni, Yeni | |
dc.contributor.advisor | Wulandari | |
dc.contributor.author | Astuti, Annisa Widia | |
dc.date.accessioned | 2022-07-19T08:38:41Z | |
dc.date.available | 2022-07-19T08:38:41Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/112629 | |
dc.description.abstract | Pada akhir tahun 2019, muncul penyakit COVID-19 yang disebabkan oleh
coronavirus. Penyakit ini kemudian dinyatakan sebagai pandemi oleh WHO pada
akhir bulan Januari 2020. Metode standar yang disarankan oleh panduan diagnosis
COVID-19 adalah menggunakan RT-PCR dan analisis citra. Namun, kedua metode
ini masih belum cukup optimal dikarenakan waktu yang dibutuhkan untuk
melakukan identifikasi COVID-19 cukup lama, yaitu 4-6 jam menggunakan RTPCR dan 21,5 menit menggunakan analisis citra. Selain itu, sensitivitas yang
dihasilkan menggunakan metode RT-PCR masih cukup rendah, yaitu 71%.
Penelitian ini bertujuan untuk menerapkan algoritme texture analysis untuk
melakukan identifikasi COVID-19 berbasiskan citra computed tomography. Fitur
klinis yang didapatkan pada texture analysis kemudian diklasifikasikan
menggunakan metode support vector machine. Hasil klasifikasi yang didapatkan
cukup baik dengan nilai precision 86%, recall (sensitivitas) 99%, dan accuracy
91%. Diharapkan metode ini dapat membantu tenaga medis dalam melakukan
proses identifikasi COVID-19. | id |
dc.description.abstract | At the end of 2019, the COVID-19 disease caused by the coronavirus
emerged. The disease was later declared a pandemic by WHO at the end of January
2020. The standard method recommended by the COVID-19 diagnosis guide is to
use RT-PCR and image analysis. However, these two methods are still not optimal
enough because the time needed to identify COVID-19 is quite long, i.e. 4-6 hours
using RT-PCR and 21.5 minutes using image analysis. In addition, the sensitivity
produced using the RT-PCR method is still quite low, i.e. 71%. This study aims to
apply a texture analysis algorithm to identify COVID-19 based on computed
tomography images. The clinical features obtained in the texture analysis are then
classified using the support vector machine method. The classification results
obtained are quite good with 86% precision, 99% recall (sensitivity), and 91%
accuracy. It is hoped that this method can help medical personnel in carrying out
the COVID-19 identification process. | id |
dc.language.iso | id | id |
dc.publisher | IPB University | id |
dc.title | Ekstraksi Fitur Klinis Citra CT Scan Penderita COVID-19 Menggunakan Metode Texture Analysis | id |
dc.type | Undergraduate Thesis | id |
dc.subject.keyword | computed tomography | id |
dc.subject.keyword | COVID-19 | id |
dc.subject.keyword | identification | id |
dc.subject.keyword | texture analysis | id |