Skripsi

IMPLEMENTASI METODE DECISION TREE DAN FUZZY DECISION TREE DALAM PENGKLASIFIKASIAN HAMA DAN PENYAKIT TANAMAN JAGUNG

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Penilaian

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Corn is one of the staple food sources and has an important role in the economy because of its various functions, but during the growth period the corn plant is very vulnerable to pests and diseases. Losses due to pests and diseases are not small, some of them can even cause crop failure. Classification is done to help farmers control pests and diseases during the growth period. Detection of pests and diseases of corn plants can be done by digital image processing. The data used are 7052 images with 3 predictor variables and 1 target variable which has 7 labels, including 3 types of pests, 3 types of diseases, and healthy corn plants. Classification prediction of pests and diseases of maize plants using the Decision Tree and Fuzzy Decision Tree methods. In the classification process using the Decision Tree and Fuzzy Decision Tree methods, it was found that the predictor variable G has a greater influence than the predictor variables R and B, so that the predictor variable G is designated as the root node in the decision tree. This study shows that the Decision Tree method produces an accuracy of 89.33%, a precision 54.55%, a recall of 62.87%, and an fscore of 55.05%, while the Fuzzy Decision Tree method produces an accuracy of 89.65%, a precision of 52.32%, a recall of 64.74%, and an fscore of 55.08%. The level of accuracy using the Fuzzy Decision Tree method is 0.32%, the precision is lower 2.23%, the recall is 1.87% higher, and the fscore is higher 0.03% than the Decision Tree Method. Keywords: Corn, Digital Image, Classification, Decision Tree, Fuzzy Decision Tree

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002166	T107127	T1071272023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1071272023

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2023

Collation

xiv, 63 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

511.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Logika Matematika
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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