TY  - THES
N1  - Pembimbing: Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.
ID  - digilib67440
UR  - https://digilib.uin-suka.ac.id/id/eprint/67440/
A1  - Irma Eryanti Putri, NIM.: 22206052007
Y1  - 2024/08/23/
N2  - 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%.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - CNN
KW  -  ResNet50
KW  -  MobileNetV2
KW  -  DenseNet169
KW  -  Xception
KW  - 
InceptionV3 dan EfficientNetB0
KW  -  Ensembel
KW  -  Random Search
M1  - masters
TI  - MODEL ENSEMBEL CONVOLUTIONAL NEURAL NETWORK (CNN) MENGGUNAKAN RANDOM SEARCH UNTUK DETEKSIPNEUMONIA BERDASARKAN CHEST X-RAY
AV  - restricted
EP  - 136
ER  -