MODEL ENSEMBEL CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN RANDOM SEARCH UNTUK DETEKSIPNEUMONIA BERDASARKAN CHEST X-RAY

Irma Eryanti Putri, NIM.: 22206052007 (2024) MODEL ENSEMBEL CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN RANDOM SEARCH UNTUK DETEKSIPNEUMONIA BERDASARKAN CHEST X-RAY. Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Pneumonia is an infection caused by bacteria, viruses and fungi in the air sacs in the lungs, which can cause disorders such as coughing up phlegm, fever, chills, nausea, vomiting, fatigue and shortness of breath. If pneumonia is not treated quickly and appropriately it can result in death, therefore early detection of pneumonia is a solution to prevent pneumonia. This research detects pneumonia from Chest X-ray data with an ensemble of Convolutional Neural Network (CNN) models such as ResNet50, MobileNetV2, DenseNet169, To detect pneumonia, this research has several stages, namely collecting Chest The results of pneumonia detection with the Ensemble CNN model using Random Search on the Chest X-ray dataset obtained training accuracy of 0.92%, testing accuracy of 0.92% and validation accuracy of 0.93%. Evaluation of the Confusion Matrix Ensemble CNN model using Random Search obtained an accuracy of 0.94%, recall of 1.00%, precision of 0.89% and f1-score of 0.94%.

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