Email this article Aplikasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Retakan Beton
Farida Asriani(1*), Gandjar Pamudji(2), Hesti Susilawati(3), Firdauz Tri Anggoro(4)(1) Jurusan Teknik Elektro, Fakultas Teknik, Universitas Jenderal Soedirman, Purwokerto
(2) Jurusan Teknik Sipil, Fakultas Teknik, Universitas Jenderal Soedirman, Purwokerto
(3) Jurusan Teknik Elektro, Fakultas Teknik, Universitas Jenderal Soedirman, Purwokerto
(4) Jurusan Teknik Elektro, Fakultas Teknik, Universitas Jenderal Soedirman, Purwokerto
(*) Corresponding Author
Abstract
Beton menjadi bahan utama dari kebanyakan kontruksi bangunan. Timbulnya sebuah retakan atau kerusakan struktur dari beton tersebut sangat berpengaruh terhadap struktur bangunan secara keseluruhan karena mampu memperpendek umur dari bangunan tersebut. Dari hal tersebut, diperlukannya pengawasan secara rutin terhadap kondisi struktur beton sehingga dapat dilakukan perencanaan pemeliharaan di masa depan. Pada paper ini penulis menerapkan teknologi sistem cerdas terhadap pendeteksian keretakan beton. Penerapan Deep Learning dengan arsitektur Convolutional Neural Networks dengan model MobileNet V1 dan Inception V3 dan ResNet-50 untuk melakukan pengklasifikasian kondisi keretakan dari sebuah masukan gambar visual. Deteksi keretakan beton yang dilakukan dikelompokkan dalam tiga kelas yaitu retak besar, retak kecil dan tidak retak. Dari hasil training dan validasi yang telah dilakukan CNN dengan model mobileNet V1 memberikan hasil akurasi yang terbaik yaitu 0,8924 untuk akurasi pelatihan dan 0,8899 untuk akurasi validasi
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