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TEKNIK OVERSAMPLING UNTUK MEMPREDIKSI STATUS PASIEN BEDAH TORAKS MENGGUNAKAN METODE DECISION TREE C4.5

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Penilaian

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Lung cancer is one of the diseases with the highest mortality rate in the world. The cause of lung cancer is closely related to smoking habits. There are several types of lung cancer treatment, one of which is surgery. Thoracic surgery is one of the most common operations performed on patients with lung cancer. One of the main problems in treating lung cancer patients is deciding whether to undergo thoracic surgery because of the high risk of death. Therefore, it is necessary to predict the survival of patients with lung cancer after thoracic surgery. This study uses secondary data obtained from UCI Machine Learning which has 16 variables with 470 data. In the data there is a class imbalance, the class balancing technique used is the Oversampling technique. The method used to predict the status of thoracic surgery patients is the Decision Tree C4.5 method. The results of this study obtained a value on Decision Tree C4.5 with an accuracy of 88.30%, recall of 47.37%, precision of 90%, F-Measure of 62.1% and G-Mean of 68.36%. Decision Tree C4.5 with Oversampling technique with 88.30% accuracy, 78.95% recall, 68.18% precision, 73.17 F-Measure and 84.6% G-Mean.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207000317	T63540	T635402022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T635402022

Publisher

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

Collation

xvi, 58 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.530 7

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)

Matematika statistikal deskriptif
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

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