• Vincentius Praskatama Informatics Engineering, Faculty of Computer Science, University of Dian Nuswantoro, Indonesia
  • Christy Atika Sari Informatics Engineering, Faculty of Computer Science, University of Dian Nuswantoro, Indonesia
  • Eko Hari Rachmawanto Informatics Engineering, Faculty of Computer Science, University of Dian Nuswantoro, Indonesia
  • Noorayisahbe Mohd Yaacob Malaysia-Japan International Institute of Technology (MJIIT), University of Technology Malaysia (UTM), Malaysia
Keywords: CNN, Deep Learning, Neural Network, Pneumonia, X-Ray


Pneumonia is condition which our lungs become inflamed due to infection from viruses, bacteria, or fungi. Pneumonia can affect anyone, both adults and children. Because of this, prevention of pneumonia is important. Prevention can be done by the process of maintain our immunity and lungs. In this study, had been done classify pneumonia based on X-ray images. This study using X-ray images dataset with total data is 5840 images in .jpg extensions. With a total number of images from training data is 5216 images and number of images from the test data is 624 images. The dataset that used in this research has 2 main classes, namely class normal and pneumonia. Normal class indicates that the X-Ray results are not detected with pneumonia. While the pneumonia class indicates that the processed X-Ray results are diagnose affected by pneumonia. The purpose of this research is building model that can be used to classify pneumonia based on X-Ray images. The classification process carried out in this study uses the Convolutional Neural Network method. The purpose of using the CNN method in the classification process of this research is because, in the process, CNN can extract features automatically and independently, so that the data provided does not need to be preprocessing first, but the data still produces good extraction features and can provide accurate classification results.  The results from the testing process is carried out to run or perform in the pneumonia classification process, the CNN model built obtained a classification test accuracy of 87.82051205635071%.


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How to Cite
V. Praskatama, C. A. Sari, E. H. Rachmawanto, and N. Mohd Yaacob, “PNEUMONIA PREDICTION USING CONVOLUTIONAL NEURAL NETWORK”, J. Tek. Inform. (JUTIF), vol. 4, no. 5, pp. 1217-1226, Oct. 2023.