KLASIFIKASI CITRA KEBUN RESOLUSI TINGGI DENGAN MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK BERDASARKAN EKSTRAKSI CIRI

LESTARI, BELLA and Assaidah, Assaidah and Koriyanti, Erry (2023) KLASIFIKASI CITRA KEBUN RESOLUSI TINGGI DENGAN MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK BERDASARKAN EKSTRAKSI CIRI. Undergraduate thesis, Univercity Sriwijaya.

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Abstract

Manual classification of garden types is carried out only based on direct visual observation of garden images armed with experience and knowledge gained previously. Many problems arise when the process of classifying garden types is carried out manually, including inaccurate and non-uniform. This is because human vision has weaknesses and limitations. Based on this, this study was made a program to classify garden types based on the extraction of size and shape characteristics with the help of computers that utilize image processing and neural network methods Backpropagation Neural Network. The classification of garden types is carried out on 4 types of gardens, namely chili gardens, long bean gardens, banana gardens, and cucumber gardens. The application of garden type classification is carried out through 5 processes, namely collecting garden image data, extracting the size and shape characteristics of garden image data, conducting data training using Backpropagation Neural Network artificial neural networks and data testing. Trials that have been conducted with 80 training image data and 20 test image data show that the neural network backpropagation model used for machine learning in this study has successfully classified plant species contained in the image of the garden. From the results of aerial photo image data used as a map has a spatial resolution of 2.34 cm/pix.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	pengolahan citra, jaringan syaraf tiruan Backpropagation Neural Network, ekstraksi ciri ukuran dan bentuk, pemetaan, citra drone.
Subjects:	Q Science > QC Physics > QC1-75 General Q Science > QC Physics > QC1-999 Physics Q Science > QC Physics > QC1-999 Physics > C331.S67 Heat--Radiation and absorption. Heat engineering
Divisions:	08-Faculty of Mathematics and Natural Science > 45201-Physics (S1)
Depositing User:	Bella Lestari
Date Deposited:	31 May 2023 06:57
Last Modified:	31 May 2023 06:57
URI:	http://repository.unsri.ac.id/id/eprint/106347

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