Skripsi

PENANGANAN DATA HILANG MENGGUNAKAN SELEKSI FITUR C4.5 PADA KLASIFIKSI PENYAKIT JANTUNG MENGGUNAKAN BACKPROPAGATION

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Penilaian

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Missing Value can result in weak prediction accuracy, so it needs to be addressed. The way to deal with lost data is to use the deletion method. This method requires a classification algorithm to select the most influential features. In this study, the University of California Irvine (UCI) heart attack dataset was used which has a weakness, namely it has missing data on several features. This study uses the C4.5 Algorithm to overcome missing data by selecting important features and using Backpropagation for testing. The test results obtained an accuracy value of 78.9116 % for testing without the application of the C4.5 algorithm on missing data, while the accuracy value of 80.6112% for testing with the application of the C4.5 algorithm on missing data using 10 features in the test. It can be concluded that the application of the C4.5 algorithm to select influential features and the use of Backpropagation for testing can improve accuracy.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207001020	T69097	T690972022	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T690972022

Publisher

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

Collation

xii, 53 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.507

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)

Jurusan Matematika
Matematika Statistikal

Specific Detail Info

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Statement of Responsibility

PITRIA

Other version/related

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