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Perbandingan Convolutional Neural Network VGG16 dan ResNet34 pada Sistem Klasifikasi Sampah Botol

1Department of Computer Science, Universitas Brawijaya, Indonesia

2Faculty of Computer Science, Universitas Brawijaya, Jl. Veteran No.8, Ketawanggede, Kec. Lowokwaru, Kota Malang, Indonesia 65145, Indonesia

Received: 7 Jan 2021; Published: 31 Jan 2022.
Open Access Copyright (c) 2021 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract
Hampir semua botol minuman kemasan yang beredar di masyarakat terbuat dari bahan plastik dikarenakan plastik merupakan bahan yang murah dan mudah dibentuk. Plastik adalah bahan non-organik yang sulit diuraikan sehingga botol plastik dapat menyebabkan pencemaran lingkungan. Sehingga diperlukan suatu solusi yang efektif untuk mengatasi kerusakan lingkungan yang disebabkan oleh sampah botol plastik. Salah satu solusi yag dapat dilakukan yaitu melakukan klasifikasi dan daur ulang sampah botol plastik. Pengklasifikasian sampah botol plastik dan sampah botol bukan plastik ke dalam kategori yang ditentukan sesuai dengan persyaratan kemudian didaur ulang agar dapat diolah kembali agar tidak merusak lingkungan. Artikel ini mengusulkan model VGG16 dan ResNet34 berbasis deep learning menggunakan CNN (Convolutional Neural Network) untuk mengidentifikasi dan mengklasifikasikan sampah botol. Berdasarkan hasil pengujian menggunakan Convolutional Neural Network, arsitektur VGG16 memiliki akurasi sebesar 90% dan ResNet34 memiliki akurasi sebesar 50% pada klasifikasi botol plastik dan bukan botol plastik. Masing-masing arsitektur menggunakan 10 epoch, 32 batch, 1655 gambar.
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Keywords: klasifikasi; CNN; VGG16; ResNet34

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