Klasifikasi Citra X-Ray Pneumonia Menggunakan Convolutional Neural Network (CNN) dengan Eksplorasi Tekstur Gabor Filter
DOI:
https://doi.org/10.30865/json.v7i4.9704Keywords:
Pneumonia, Chest X-ray, Convolutional Neural Network, Gabor Filter, sEMMAAbstract
Pneumonia merupakan salah satu penyakit pernapasan yang dapat dideteksi melalui citra X-ray dada, ditandai dengan munculnya infiltrat pada paru-paru. Proses diagnosis manual oleh tenaga medis memerlukan waktu dan berpotensi menghasilkan subjektivitas, sehingga diperlukan pendekatan berbasis kecerdasan buatan. Penelitian ini bertujuan mengembangkan model klasifikasi pneumonia menggunakan metode Convolutional Neural Network (CNN) yang dikombinasikan dengan eksplorasi Gabor Filter untuk analisis tekstur citra. Metodologi yang digunakan adalah sEMMA (Sample, Explore, Modify, Model, Assess). Dataset yang digunakan bersumber dari Kaggle berjumlah 5.216 citra yang terbagi menjadi dua kelas, yaitu normal dan pneumonia, dengan pembagian data training dan validation menggunakan rasio 80:20. Tahap preprocessing meliputi resize citra, konversi grayscale, augmentasi data, normalisasi, serta penerapan Gabor Filter sebagai analisis tekstur. Model CNN dibangun secara kustom dan dilatih menggunakan optimizer Adam. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 90,61%, presisi 98,97%, recall 88,12%, dan F1-score sebesar 0,9323. Selain itu, nilai AUC-ROC sebesar 0,9854 dan AUC-PR sebesar 0,9950 menunjukkan kemampuan klasifikasi yang baik. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan berpotensi mendukung diagnosis pneumonia berbasis citra medis.
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