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Arsitektur Resnet-152 dengan Perbandingan Optimizer Adam dan RMSProp untuk Mendeteksi Penyakit Paru – Paru
Sari
ABSTRAK
Penyakit paru-paru pada manusia sering terjadi di seluruh dunia umumnya terjadi karena polusi udara dan asap rokok. Untuk mendeteksi penyakit paru-paru manusia ini diperlukan kemampuan secara tepat dengan menggunakan Chest X-Rays (CXR). CXR umumnya sulit dibedakan oleh manusia, maka dari itu pada penelitian ini menerapkan model Deep Learning sebagai sarana untuk mendeteksi penyakit paru-paru manusia melalui citra CXR. Eksperimen dilakukan dengan menggunakan model arsitektur yaitu ResNet-152 serta 2 optimizer yaitu Adam dan RMSProp. Selain itu, pengujian model dilakukan dengan menggunakan accuracy, precision, recall, f1-score, specitifity dan grafik Receiver Operating Characteristic (ROC). Pada penelitian ini menunjukkan bahwa model ResNet-152-R10 yang memiliki tingkat accuracy, precision, recall, f1-score dan specitifity terbaik yaitu masing-masing 92%, 94%, 92%, 93% dan 96,75%.
Kata kunci: adam optimizer, rmsprop optimizer, resnet-152, penyakit paru-paru, pneumonia, lung opacity
ABSTRACT
Lung disease in humans often occurs throughout the world generally occurs due to air pollution and cigarette smoke. To detect human lung disease, it requires the ability to be precise using Chest X-Rays (CXR). CXR is generally difficult to distinguish by humans, therefore this study applies the Deep Learning model as a means of detecting human lung disease through CXR imagery. Experiments were carried out using the architectural model, namely ResNet-152 and 2 optimizers, namely Adam and RMSProp. In addition, model testing is carried out using accuracy, precision, recall, f1-score, specificity and Receiver Operating Characteristic (ROC) graphs. This study shows that the ResNet-152-R10 model has the best levels of accuracy, precision, recall, f1-score and specificity, namely 92%, 94%, 92%, 93% and 96.75% respectively.
Keywords: adam optimizer, rmsprop optimizer, resnet-152, lung disease, pneumonia, lung opacity
Teks Lengkap:
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DOI: https://doi.org/10.26760/mindjournal.v7i2.139-150
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