Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307004733	T126771	T1267712023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1267712023

Publisher

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

Collation

xii, 55 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

518.107

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)

Algoritma
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

PITRIA

Other version/related

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