Klasifikasi Citra Jenis Pesawat Menggunakan Algoritma CNN Dengan Arsitektur ResNet
Abstract
Jenis pesawat terbang beragam berdasarkan bentuk, ukuran, dan tujuan penggunaannya, sehingga sulit untuk diklasifikasikan dan menjadikannya sebuah tantangan terutama dalam pengembangan teknologi citra. Dalam penelitian ini, algoritma CNN dengan arsitektur ResNet digunakan untuk mengembangkan sistem klasifikasi citra jenis pesawat. Model dibangun dan diuji pada dataset pribadi yang terdiri dari 4.520 citra dari delapan kelas jenis pesawat. Model dilatih dalam dua skenario (dengan dan tanpa augmentasi) dan dua ukuran batch (16, 32), tiga varian layer ResNet (50, 101, 152) menghasilkan dua belas model. Hasil evaluasi menunjukkan bahwa model terbaik dari ResNet-152 dengan augmentasi mampu mencapai f1-score hingga 0.954, membuktikan bahwa model CNN-ResNet yang dikembangkan dapat mengklasifikasikan citra pesawat secara efektif. Meski begitu, kesalahan masih terjadi pada kelas-kelas yang memiliki kemiripan visual, sehingga dibutuhkan eksperimen lanjutan dengan menguji kombinasi arsitektur dan parameter pelatihan yang lebih optimal untuk meningkatkan akurasi dan kemampuan generalisasi model yang lebih baik.
Kata kunci: Klasifikasi, Citra, Pesawat, CNN, ResNet, Augmentasi
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