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

FITRIANTI, DWI and Desiani, Anita and Andriani, Yuli (2022) PENANGANAN DATA HILANG MENGGUNAKAN SELEKSI FITUR C4.5 PADA KLASIFIKSI PENYAKIT JANTUNG MENGGUNAKAN BACKPROPAGATION. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Algoritma C4.5, Data Hilang, Backpropagation, dan Akurasi
Subjects:	Q Science > QA Mathematics > QA1-43 General Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Dwi Fitrianti
Date Deposited:	18 Apr 2022 06:43
Last Modified:	18 Apr 2022 06:43
URI:	http://repository.unsri.ac.id/id/eprint/69097

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