PREDIKSI RISIKO PENYAKIT JANTUNG KORONER DENGAN METODE ENSEMBLE MENGGUNAKAN ALGORITMA NAIVE BAYES, DECISION TREE C4.5 DAN REGRESI LOGISTIK BINER

MAULIA, OLIVIA RIZKI INTAN and Resti, Yulia and Zayanti, Des Alwine (2021) PREDIKSI RISIKO PENYAKIT JANTUNG KORONER DENGAN METODE ENSEMBLE MENGGUNAKAN ALGORITMA NAIVE BAYES, DECISION TREE C4.5 DAN REGRESI LOGISTIK BINER. Undergraduate thesis, Sriwijaya University.

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Abstract

Coronary heart disease is one of the causes with high amount of death in Indonesia. Coronary heart disease is a coronary atherosclerotic disease that causes narrowing of blood vessels. Therefore, there are many who conduct research on coronary heart disease both on a large and small scale which is carried out by a classification process using a certain algorithm. This research aims to predict the risk of heart disease using the Ensemble method by combining the three classification algorithms. The data used in this study has 16 variables with a total data of 4238 data. The classification prediction uses the Ensemble Majority Vote method by combining the Naive Bayes algorithm, Decision Tree C4.5 and Binary Logistics Regression. The results of this study indicate that the prediction of the risk of heart disease using the Ensemble Majority Vote method obtains an accuracy rate of 84.79%, a precision of 86.01%, and a recall of 97.91%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Coronary Heart Disease, Ensemble Majority Vote, Naive Bayes, Decision Tree, Binary Logistics Regression
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.C663 Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	OLIVIA RIZKI INTAN MAULIA
Date Deposited:	30 Sep 2021 01:15
Last Modified:	30 Sep 2021 01:15
URI:	http://repository.unsri.ac.id/id/eprint/55008

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