@mastersthesis{digilib67440,
           month = {August},
           title = {MODEL ENSEMBEL CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN RANDOM SEARCH UNTUK DETEKSIPNEUMONIA BERDASARKAN CHEST X-RAY},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22206052007 Irma Eryanti Putri},
            year = {2024},
            note = {Pembimbing: Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.},
        keywords = {CNN, ResNet50, MobileNetV2, DenseNet169, Xception,
InceptionV3 dan EfficientNetB0, Ensembel, Random Search},
             url = {https://digilib.uin-suka.ac.id/id/eprint/67440/},
        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\%.}
}