CD Skripsi

Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode Cnn

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ABSTRACT
Mushrooms are plants that do not have true roots and leaves. There are
many types of mushrooms that have been identified worldwide, with various
shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy,
health, and others. One of the benefits of mushrooms is as a food source in
Indonesia, but not all types can be consumed. To identify mushroom species, the
concepts of Genus and species can be used. The concept of Genus is considered
easier because it groups mushroom types based on similar morphological
characteristics. Therefore, a model is needed to classify mushrooms based on
consumable and toxic genera. The method used in this research is Convolution
Neural Network (CNN) due to its good predictive results in image recognition.
The model in the research utilizes three convolution layers, three MaxPooling
layers, and two dropout layers. The use of dropout aims to reduce overfitting in
the model. The research uses a dataset of 1200 images with a training and testing
data ratio of 70:30, resulting in 840 training data and 360 testing data. The best
accuracy achieved by this model is 89% for training and 82% for validation.
Therefore, it can be concluded that the model is able to classify mushrooms based
on Genus using the CNN method.
Keyword : Mushroom, Genus, CNN, Edible, Toxic

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1907113779

Penerbit

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

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1907113779

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

Lampiran Berkas

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