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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains
Tri Rachmad Saputro
Universitas Pamulang
Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences

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IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK PADA PENYAKIT PNEUMONIA (STUDI KASUS : DINAS KESEHATAN KOTA TANGERANG SELATAN) Tri Rachmad Saputro; Bambang Santoso
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 03 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

To carry out the process of diagnosing pneumonia requires a fast and accurate process. The problem that occurs is that the diagnosis of pneumonia is still done manually. This causes a long time to wait for the availability of specialists, so technology is needed that can help pulmonary specialists to analyze early X-rays quickly and accurately with the use of computer-based information technology and data. It is hoped that this research will provide a solution to the problem and also help speed up the initial diagnosis using the CNN method. The CNN method was chosen because it has a high introduction to the deep learning process. In the CNN method there are various kinds of architectures that are generated through the experimental process that has been carried out by previous researchers. The problem that occurs is that the diagnosis of pneumonia is still done manually. This causes a long time to wait for the availability of specialists, so technology is needed that can help pulmonary specialists to analyze early X-rays quickly and accurately with the use of computer based information technology and data. This Pneumonia Detection System which is made using Python and uses the Convolutional Neural Network method can predict Pneumonia Disease using X-ray images with an Accuracy Rate of 91%, which can recommend to expert doctors and help the public recognize pneumonia.
Co-Authors Bambang Santoso