@thesis{thesis,
		author={Saputra  Agung Eka},
		title ={Klasifikasi Penderita Pneumonia Berdasarkan Citra Chest X-Ray Menggunakan Metode Convolutional Neural Network},
		year={2021},
		url={http://repository.universitasbumigora.ac.id/1062/},
		abstract={Pneumonia is the leading infectious cause of death in children worldwide. Pneumonia killed 808,694 children under the age of 5 years in 2017. Therefore, early diagnosis of pneumonia patients is needed so that treatment can be carried out more effectively so that it can reduce the death rate that occurs. Diagnosis using Chest X-Ray (CXR) or chest X-ray is the most frequently used option because it is more affordable. It's just that the lack of diagnosing using CXR, namely the disease is difficult to detect and it takes a long time before medical personnel or doctors diagnose the disease suffered by the patient.
Convolutional Neural Network (CNN) is a deep learning (DL) method that can be used to detect and recognize an object in a digital image. Thus, the author took the initiative to try to classify CXR images of pneumonia patients using the CNN method and implement it on Android-based mobile devices. In this study, a training experiment or CNN model training was also conducted with 2 different sampling methods and compared the results.
The research and development method is divided into 2 stages, the first stage is to develop the model then followed by the second stage using the Waterfall method in developing mobile applications.
The results to be achieved in this study are to produce an application that is able to classify pneumonia and normal patients based on CXR}
		}