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IMPLEMENTASI METODE FUZZY DECISION TREE DALAM MENGKLASIFIKASI HAMA DAN PENYAKIT TANAMAN JAGUNG BERDASARKAN REPEATED K-FOLD CROSS VALIDATION

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Corn is a high carbohydrate plant that can be used as a staple food. The quality of corn must be considered, unhealthy corn results in less than optimal production results and results in decreased corn production. One of the causes of decreased corn quality is pests and diseases that attack corn plants. To overcome this problem, in the current era we can use computer assistance to classify pests and diseases on corn using digital image management, namely RGB images with statistical machine learning where the classification process will be carried out with the fuzzy decision tree method based on the repeated k-fold cross. validation with 10 fold and 5 repeated. The data used are 3712 photos of corn pests and diseases, consisting of 108 photos of grasshoppers, 120 photos of cob-moving pests, 1337 photos of Spodoptera frugiperda pests, 49 photos of downy mildew, 232 photos of leaf blight, 285 photos of leaf rust. and 1041 photos of healthy (non pathogenic) leaves. In this study, the results of the fuzzy decision tree method based on the average of all folds and repeated method can classify pests and diseases of corn with an accuracy of 92.48%, macro precision of 70.58%, micro-precision of 74.08%, macro recall of 63.55%, micro recall of 74.08%, the macro fscore is 66.77% and the micro fscore is 74.08%.

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Detail Information

Series Title

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Call Number

T770992022

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2022

Collation

xiv, 88 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

511.307

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)

Logika Matematika
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

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