Optimasi Model CNN Berbasis Transfer Learning Untuk Klasifikasi Pneumonia pada Citra X-Ray Dada
DOI:
https://doi.org/10.32664/smatika.v15i01.1985Kata Kunci:
DenseNet-121, Fine-Tuning, Pneumonia, Transfer LearningAbstrak
Pneumonia merupakan salah satu penyebab utama kematian anak di dunia dan diagnosisnya bergantung pada interpretasi citra radiografi dada (X-ray) yang rentan terhadap kesalahan manusia. Penelitian ini bertujuan mengoptimalkan model Convolutional Neural Network (CNN) berbasis transfer learning dengan arsitektur DenseNet-121 untuk klasifikasi pneumonia pada citra X-ray dada. Model dikembangkan menggunakan dataset dari Kaggle yang terdiri atas dua kelas: Normal dan Pneumonia. Data mengalami pra-pemrosesan berupa augmentasi dan penyeimbangan kelas. Lima strategi fine-tuning diuji, mulai dari hanya melatih classifier hingga membuka seluruh lapisan pretrained. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil menunjukkan bahwa strategi fine-tuning dengan membuka blok 3 sampai 4 (Unfreeze Block 3–4) menghasilkan performa terbaik dengan akurasi 94,39%, F1-score 95,61%, dan ROC-AUC 98,04%. Studi ini menunjukkan bahwa strategi fine-tuning selektif mampu meningkatkan performa klasifikasi secara signifikan dibanding melatih hanya classifier atau seluruh jaringan.
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