Skripsi

PERBANDINGAN KLASIFIKASI GENUS/SPECIES BUNGA MENGGUNAKAN KOMBINASI GLOBAL FEATURE DESCRIPTION DAN K-NEAREST NEIGHBOUR (K-NN) DAN RANDOM FOREST (RF)

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Penilaian

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The need for acceleration of the selection or sorting of goods has been and is being developed by industry players both at home and abroad. Products that are selected or sorted are very diverse, one of which is flowers. The selection is based on color, texture and shape because the dataset used has colors that are almost close to more than 1 type of flower so that the combination with shape and texture will make a difference to the type of flower and several studies have shown that the combination of color and texture has been proven successful in finding similar images. Image testing uses primary datasets totaling 80 images per each class, thus there are 55 ~ 54 training data and 25 ~ 26 test data (random images per each class).This study uses 3 methods to take image features with 2 stages that are distinguished from input, namely for RGB images that are converted to grayscale channels, executed by Haralick Texture and Hu Moments, while the complete RGB image is executed by Color Histogram (which in this case RGB to HSV is a characteristic. As a whole, it is taken based on the color for the object (flower) then the classification will be carried out using the k-Nearest Neighbor method and compared with the Random Forest method. Based on the test results it is found that k-Nearest Neighbor with k = 3 produces a higher predictive value of 55% compared to k = 5 and k = 7, which both produce a predictive value of 53% of the 5 flower classes tested 2. Then the comparison of methods is carried out to get a better result increase. Random Forest (RF) produces a better predictive value of 92 % with the highest precision, namely Hyacinthoides L., the highest recall was Tussilago farfara L. (species), and the highest F1-Score was Hyacinthoides L. in 5 flower classes.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000910	T39910	T399102020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T399102020

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2020

Collation

xiv, 63 hlm.; ilus., tab.: 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer
Ilmu Komputer
Global Feature Description

Specific Detail Info

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Statement of Responsibility

EM

Other version/related

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