Skripsi

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

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107004065	T58610	T586102021	Central Library (2107004065)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T586102021

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xv, 75 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrosesan data
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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