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Abstrak

Korosi merupakan permasalahan utama yang menyebabkan kerugian ekonomi yang signifikan di berbagai industri, termasuk transportasi, energi, dan manufaktur. Deteksi dini korosi sangat penting untuk mengurangi dampak negatifnya. Penelitian ini bertujuan mengembangkan sistem klasifikasi korosi otomatis berbasis Convolutional Neural Networks (CNN) dengan pendekatan transfer learning. Dua model dievaluasi, yaitu arsitektur CNN sederhana dan MobileNetV2 yang telah dilatih sebelumnya. Dataset terdiri dari gambar korosi dan non-korosi yang dibagi menjadi data pelatihan, validasi, dan pengujian. Teknik augmentasi data diterapkan untuk meningkatkan variasi dan jumlah sampel dalam proses pelatihan. Hasil eksperimen menunjukkan bahwa MobileNetV2 mencapai akurasi pengujian sebesar 95%, lebih tinggi dibandingkan CNN sederhana yang hanya mencapai 82%. Selain itu, MobileNetV2 menunjukkan performa yang lebih baik dalam mengidentifikasi kedua kelas (korosi dan non-korosi). Meskipun terdapat indikasi overfitting akibat keterbatasan dataset, pendekatan transfer learning secara signifikan meningkatkan performa klasifikasi. Sistem ini berpotensi untuk diterapkan dalam aplikasi industri secara real-time guna mengurangi kerugian ekonomi akibat korosi. Penelitian lanjutan disarankan untuk meningkatkan generalisasi model dengan menggunakan dataset yang lebih besar serta menerapkan teknik regularisasi yang lebih kuat.

Kata Kunci

Aplikasi Industri Augmentasi Convolutional Neural Network Klasifikasi MobileNetV2

Rincian Artikel

Cara Mengutip
[1]
M. H. Rizky Pratama, M. Akrom, A. P. Santosa, M. R. Rosyid, dan L. Mawaddah, “Klasifikasi Otomatis Korosi Menggunakan Convolutional Neural Network dan Transfer Learning dengan Model MobileNetV2”, Jurnal Algoritma, vol. 22, no. 1, hlm. 138–148, Mei 2025.

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