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

RAHMAWATI, TESYA and Resti, Yulia and Kresnawati, Endang Sri (2022) TEKNIK OVERSAMPLING UNTUK MEMPREDIKSI STATUS PASIEN BEDAH TORAKS MENGGUNAKAN METODE DECISION TREE C4.5. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kanker paru, Bedah toraks, Decision Tree C4.5, Oversampling
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	TESYA RAHMAWATI
Date Deposited:	02 Feb 2022 01:53
Last Modified:	02 Feb 2022 01:53
URI:	http://repository.unsri.ac.id/id/eprint/63540

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