Wahyu, Alfandi (2022) COVIDIA (COVID DIAGNOSE APPLICATION) APPLICATION OF COVID-19 PATIENT RECOGNITION USING MOBILE-BASED LUNG X-RAY IMAGES. S1 thesis, Universitas Mataram.
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[English Version] Laporan TA2 Wahyu Alfandi.pdf Restricted to Repository staff only Download (3MB) |
Abstract
Covid-19 is an infectious disease caused by acute respiratory syndrome coronavirus 2 (Sars-CoV-2). Based on data from Worldometers, as of Wednesday, 14 July 2021, there were 188,563,150 cases of Covid-19 worldwide, with 4,065,129 of them have died and 172,396,201 of them being declared cured. The government provides several methods for detecting Covid-19, including the Rapid Antibody Test, Antigen Swab Test, and PCR. These three methods still need to improve in the accuracy, time, and cost of the required testing to improve patient test results X- ray and CT scans are used. The study's results stated that using CT scans found results in 97% of cases infected with Covid-19. Other studies also obtained results of 96.5% accuracy. Even so, the use of CT scans requires expensive device setup costs. Meanwhile, the use of X-ray imagery for the detection of Covid-19 using the Convolutional Neural Networks (CNN) method obtains an accuracy of 98% so that it is considered capable of being an alternative tool for diagnosing Covid- 19 patients. Based on the problems above, the authors designed and built an information system entitled "Covidia (Covid Diagnose Application) - Application for Recognizing Covid 19 Patients Using Mobile-Based Lung X-Ray Imagery," which is expected to facilitate the process of identifying Covid-19 patients. The Extreme Programming (XP) method is used for system development. From the tests carried out, the results obtained from the unit test show that all scenarios that have been made are as desired. In performance testing, the application performs well, but optimization is still needed in memory usage. Furthermore, testing the System Usability Scale obtains a usability score of 71.38, meaning that the system feels good in use and functionality and gains end-user acceptance.
| Item Type: | Thesis (S1) |
|---|---|
| Keywords (Kata Kunci): | Covid-19, citra X-ray, CNN, Android, Xtreme Programming, System Usability Scale |
| Subjects: | T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Fakultas Teknik |
| Depositing User: | Meike Megawati |
| Date Deposited: | 21 Dec 2022 04:53 |
| Last Modified: | 21 Dec 2022 04:53 |
| URI: | http://eprints.unram.ac.id/id/eprint/33937 |
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