Pengaruh Penambahan Arsitektur Model dalam Klasifikasi Citra Bencana Alam Menggunakan Ensemble Learning
DOI:
https://doi.org/10.37859/jf.v15i2.9103
Abstract
Penelitian ini berfokus pada peningkatan akurasi klasifikasi citra bencana alam dengan mengintegrasikan Convolutional Neural Network (CNN), InceptionV3, dan InceptionResNetV2 dalam pendekatan ensemble learning. Model ini dilatih pada dataset multikelas yang terdiri dari citra empat kategori bencana: gempa bumi, banjir, kebakaran hutan, dan siklon. Pendekatan ensemble menghasilkan akurasi klasifikasi sebesar 96,02%, lebih tinggi dibandingkan model tunggal seperti CNN dengan 88,3%, InceptionV3 dengan 94,1%, dan InceptionResNetV2 dengan 92,4%. Penggunaan ensemble learning, khususnya soft voting, memungkinkan model untuk menggabungkan keunggulan dari masing-masing arsitektur, yang secara signifikan meningkatkan performa pada semua kategori bencana. Model ensemble menunjukkan kemampuan generalisasi yang lebih baik, terutama untuk kategori yang lebih sulit seperti Flood dan Earthquake, yang mana model tunggal kesulitan. Hasil juga menunjukkan peningkatan precision, recall, dan F1-score, dengan pendekatan ensemble mengurangi kesalahan klasifikasi sebesar 7,5% dibandingkan model terbaik tunggal, InceptionV3. Penelitian ini menunjukkan potensi ensemble learning untuk sistem deteksi bencana waktu nyata, khususnya dalam situasi kritis yang memerlukan akurasi tinggi dan kecepatan klasifikasi. Penelitian ini juga menekankan pentingnya kombinasi model deep learning yang beragam untuk meningkatkan kemampuan sistem dalam menangani berbagai skenario bencana sambil memastikan ketahanan dalam aplikasi dunia nyata.
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Copyright (c) 2025 Felicia Amanda Cahyadewi, Ibnu Richo Kurniawan, Irsyad Umar Fakhrizal, Fahrizal Denta Saputra, Achmad, Muhammad Naufal, Ricardus Anggi Pramunendar
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