Nashrulloh, Muhammad Atay Nadhif (2022) Impelemntasi Algoritma Convolutional Neural Network untuk Klasifikasi Citra Paru-Paru Normal dan Paru-Paru Terdampak Covid-19. Undergraduate thesis, UPN Veteran Jawa Timur.
|
Text (Cover)
18081010127.-cover.pdf Download (1MB) | Preview |
|
|
Text (BAB 1)
18081010127.-bab1.pdf Download (85kB) | Preview |
|
![[img]](http://repository.upnjatim.ac.id/style/images/fileicons/text.png) |
Text (BAB 2)
18081010127.-bab2.pdf Restricted to Registered users only until 17 January 2025. Download (774kB) |
|
|
Text (BAB 3)
18081010127.-bab3.pdf Restricted to Registered users only until 17 January 2025. Download (418kB) |
|
|
Text (BAB 4)
18081010127.-bab4.pdf Restricted to Registered users only until 17 January 2025. Download (422kB) |
|
|
Text (BAB 5)
18081010127.-bab5.pdf Download (73kB) | Preview |
|
|
Text (Daftar Pustaka)
18081010127.-daftarpustaka.pdf Download (135kB) | Preview |
Abstract
Corona Virus Disease 2019 or COVID-19 is a disease that attacks the respiratory system in humans. Shortness of breath and damage to the channel breathing is a severe symptom experienced by sufferers of this disease, symptoms usually in the form of coughing and loss of the sense of taste and smell. On In this study the authors developed a COVID-19 detection system with using x-ray images of the lungs as data. This system is created using the Convolutional Neural algorithm Network (CNN). The algorithm is designed to process two-dimensional data which is used to analyze, recognize and detect objects in the image. This study uses datasets from the open source website Kaggle with name COVID-19 Chest X-Ray Dataset. The total number of datasets is 4551 data image, before entering the dataset classification stage it will go through the stages preprocess first. System testing is done by comparing the effects of the three optimizer namely Adam optimizer, RMS Prop optimizer and SGD optimizer. From the experimental results obtained the best accuracy by using Adam as optimizer. Obtained a test accuracy value of 96%.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Contributors: |
|
||||||||||||
| Subjects: | T Technology > T Technology (General) | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Muhammad atay nadhif nashrulloh | ||||||||||||
| Date Deposited: | 18 Jan 2023 01:27 | ||||||||||||
| Last Modified: | 18 Jan 2023 01:27 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/11070 |
Actions (login required)
 |
View Item |