• Lingga Aji Andika Department of Statistics, Universitas Sebelas Maret (UNS), Indonesia
  • Hasih Pratiwi Department of Statistics, Universitas Sebelas Maret (UNS), Indonesia
  • Sri Sulistijowati Handajani Department of Statistics, Universitas Sebelas Maret (UNS), Indonesia




adaptive momentum, classification, convolutional neural network, pneumonia


Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.


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How to Cite

Andika, L. A., Pratiwi, H., & Handajani, S. S. (2019). KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM. Indonesian Journal of Statistics and Its Applications, 3(3), 331–340. https://doi.org/10.29244/ijsa.v3i3.560