CD Skripsi

Identifikasi Jenis Penyakit Daun Tanaman Jagung Menggunakan Jaringan Saraf Tiruan Berbasis Backpropagation

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Identification of the plant leaves of corn can be done manually by using human eye vision because based on the physical characteristic of the leaves of corn that are affected by the disease will undergo changes in shape and color in the leaf. However, it has a weakness when the corn leaves will be identified in number and each age has a different assessment of the color seen. In general, for the identification of diseases that attack the leaves of this corn plant is processed by utilizing Digital image processing and consists of four main parts, namely Image acquisition, Preprocessing, extraction of color traits, and classification. The image of the corn leaf is taken using the camera, with a total of 200 images of corn leaves that have been infected for training data and and testing on the system. The feature extraction method used is Color Moment as the extraction of a color feature to get the Mean, standard deviation, and Skewness values as input data for the neural network process. The classification method on this system uses a Backpropagation-based neural network with Matlab R2018a. The result of identification of disease types of corn plant leaves are four: leaf spots, leaf bulai, leaf blight, and leaf rust. The system is able to detect the disease of corn plant leaves with an accuracy rate of 90% and an error of 10%.

Keyword : Image Processing, Preprocessing, Color Moment, Backpropagation, Matlab R2018a

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

07 04. 119 (0057)

Penerbit

Pekanbaru : Universitas Riau – Fakultas Teknik – Teknik Informatika., 2019

Deskripsi Fisik

xi, 71 hlm.; ill.; 29 cm

Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

07 04. 119 (0057)

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

ELEKTRO

Info Detail Spesifik

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Pernyataan Tanggungjawab

DAUS

Versi lain/terkait

Lampiran Berkas

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