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KLASIFIKASI PENENTUAN PENERIMA VAKSIN COVID-19 MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN) BERBASIS EUCLIDEAN DISTANCE

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The Indonesian government created regulations regarding the implementation of Covid-19 vaccination to reduce the rate of spread after the COVID-19 pandemic was declared on March 11, 2020, by WHO. The Covid-19 vaccine is one form of prevention to avoid the viral pathogen that causes Corona disease. The number of treatments that must be carried out by vaccinators makes the tendency for accuracy to be reduced in giving appropriate actions in identifying the health conditions of prospective recipients. Therefore, this study aims to build a classification application for determining vaccine recipients by implementing the K-Nearest Neighbor (KNN) method using the Euclidean Distance algorithm. Classification is based on input data criteria that have been determined and produces classification outputs in the form of two classes, namely accepted and rejected in administering the Covid-19 vaccine. From the results of testing the eight K values on KNN using a confusion matrix, it is found that this classification can provide the best system performance at k = 1, k = 3, k = 5 with the highest accuracy of 97.3%, the smallest error rate of 2.7%, the highest precision is 96.8%, and the highest recall is 96.2%.

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Detail Information

Series Title

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Call Number

T824792022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xviii, 126 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

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)

Pemrosesan data
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

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