Skripsi

PENGOLAHAN IMAGE PROCESSING DAN JARINGAN SYARAF TIRUAN UNTUK MENENTUKAN KEMATANGAN BUAH BELIMBING MANIS (Averrhoa carambola L.)

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Penilaian

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This study aims to determine the maturity of sweet star fruit with non-destructive methods of digital image processing based on artificial neural networks based on color analysis of sweet star fruit using a camera. To increase the accuracy of the research, the multilayer backpropagation method is used by conducting training to get the existing boundaries so that when testing is on the desired target. Non-destructive image processing to determine the ripeness of sweet star fruit with a computer program based on measurements of the RGB color model. Image processing on 45 star fruit will produce 90 sample images. The factors that were analyzed using the Rancang Acak Kelompok Faktorial Method (RAKF) used were the Super Grade, A, B treatments and the mature, raw, and overripe levels of maturity. Feed-forward backpropagation is used as a learning algorithm for an artificial neural network model with a logsig activation function to predict brix, hardness, total sugar, and water content using the Matlab R2018a graphical user interface (GUI) program. Parameter testing on the results of laboratory tests showed that the parameters of sugar content, water content were significantly different from factor B, the total acid parameter was significantly different from interaction, and the hardness parameter was not significantly different from factors and interactions. Development of a training artificial neural network model using 3 inputs (Red, Green, Blue) with an MSE value of the hardness parameter of 9.9299 with the largest MSE value compared to the other parameters. The MSE value for total acid parameters was 1.4483 x 10-6, water content was 4.0154 x 10-4, and total sugar was 9.3754 x 10-2.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000809	T87928	T879282023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T879282023

Publisher

Inderalaya : Prodi Teknik Pertanian, Fakultas Pertanian Universitas Sriwijaya., 2023

Collation

xvii, 41 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

631.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Pertanian
Alat-alat, Mesin dan Perlengkapan Pertanian

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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