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

PERMATA, NUR ANNISA and Yunita, Yunita and Miraswan, Kanda Januar (2022) KLASIFIKASI PENENTUAN PENERIMA VAKSIN COVID-19 MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN) BERBASIS EUCLIDEAN DISTANCE. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Vaksin Covid-19, klasifikasi, K-Nearest Neighbor (KNN), Euclidean Distance, Confusion Matrix
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Nur Annisa Permata
Date Deposited:	23 Nov 2022 04:47
Last Modified:	23 Nov 2022 04:47
URI:	http://repository.unsri.ac.id/id/eprint/82479

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