CD Skripsi

Klasifikasi Jenis Sampah Berbasis Cnn Menggunakan Metode Transfer Learning Resnet-50

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ABSTRACT
Waste is a problem that never ends when discussed. Indonesia itself is listed as the second largest waste producing country in the world after China. This is proven by the increase in the amount of waste in Indonesia by 0.8 million tons from 67 million tons in 2019 to 67.8 million tons in 2020. To overcome this waste problem, the best solution is needed, such as creating a classification system for types of waste that can distinguish kind of garbage automatically. This classification system is useful for making it easier to categorize types of waste in the waste recycling process. In creating a CNN-based image classification system using the resnet-50 transfer learning method which functions to speed up the training process. The dataset used consists of 200 datasets which are divided into three classes, namely Inorganic, Organic, and Medical. After collecting the dataset, it then goes to the classification stage which consists of preprocessing training and testing. This research was carried out using the confusion matrix method which obtained precision results of 99%, recall of 99%, and F1-Score of 99%, while the validation test produced an accuracy of 93%.
Keywords : Classification, Garbage, Convolutional Neural Network, ResNet-50, Deep Learning, Machine Learning.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1807125021

Penerbit

Pekanbaru : Universitas Riau – Fakultas Teknik – Teknik Elektro., 2023

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1807125021

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK ELEKTRO

Info Detail Spesifik

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Pernyataan Tanggungjawab

DAUS

Versi lain/terkait

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