PERBANDINGAN DECISION TREE ALGORITMA ID3 DAN C4.5 UNTUK KLASIFIKASI KANKER SERVIKS BERDASARKAN SAMPLE BOOTSTRAPPING

FADILAH, TRI AJENG and Dewi, Novi Rustiana and Eliyati, Ning (2023) PERBANDINGAN DECISION TREE ALGORITMA ID3 DAN C4.5 UNTUK KLASIFIKASI KANKER SERVIKS BERDASARKAN SAMPLE BOOTSTRAPPING. Undergraduate thesis, Sriwijaya University.

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Abstract

Cervical cancer is one of the malignant tumors in women that attacks the female reproductive organs. There are various methods of preventing cervical cancer, one of which is conducting a pap smear screening test which is done by taking or collecting cervical cell samples on the cervical uterine wall. This study discusses the comparison of classification methods on cervical cancer data using the decision tree method of ID3 and C4.5 algorithms based on sample bootstrapping. Decision tree is a method in machine learning used for data classification and prediction that uses decision tree creation to find solutions to a problem. The precision produced by the ID3 algorithm is 98,44%, 95,05%, 94,90%, and 94,96% while for C4.5 it is 95,72%, 87,05%, 86,63%, and 86,72%. Based on the results obtained it was concluded that the ID3 algorithm is better than the C4.5 algorithm.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kanker Serviks,Algoritma
Subjects:	Q Science > QA Mathematics > QA1-43 General Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	TRI AJENG FADILAH
Date Deposited:	21 Aug 2023 02:03
Last Modified:	21 Aug 2023 02:03
URI:	http://repository.unsri.ac.id/id/eprint/126771

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