PERBANDINGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR DALAM MENGKLASIFIKASI PENYAKIT JANTUNG

PRATIWI, MAYTI and Utami, Alvi Syahrini and Rodiah, Desty (2021) PERBANDINGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR DALAM MENGKLASIFIKASI PENYAKIT JANTUNG. Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease is the number one deadly disease in the world. However, most patients with heart disease do not know the initial symptoms that are felt and not a few people with coronary heart disease die due to a heart attack. This has prompted a lot of research on heart disease, one of which uses computer-based methods. This method is widely developed with the help of intelligent computing capable of processing large amounts of data. Processing large amounts of data can be done by classification using certain algorithms so that the results are fast and accurate. In this study, a comparison of the classification of heart disease was carried out using the Naïve Bayes and K-Nearest Neighbor methods. Tests were carried out with different percentages of data and the results obtained an average accuracy of 63,94%, precision 67,97%, Recall 68,81% and F-Measure 63,83% for Naïve Bayes. Meanwhile, for K-Nearest Neighbor, the average accuracy is 17,7%, precision is 14,34%, Recall is 8,14% and F-Measure is 9,54%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Penyakit Jantung, Naive Bayes, K-Nearest Neighbor
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Mayti pratiwi
Date Deposited:	03 Dec 2021 04:38
Last Modified:	03 Dec 2021 04:38
URI:	http://repository.unsri.ac.id/id/eprint/58610

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