Klasterisasi Persebaran Virus Corona (Covid-19) Di DKI Jakarta Menggunakan Metode K-Means

Fountain of Informatics Journal

View Publication Info
 
 
Field Value
 
Title Klasterisasi Persebaran Virus Corona (Covid-19) Di DKI Jakarta Menggunakan Metode K-Means
 
Creator Solichin, Achmad; Universitas Budi Luhur
Khairunnisa, Khansa; Universitas Budi Luhur
 
Subject corona, jakarta, klasterisasi, k-means
 
Description AbstrakCorona virus (COVID-19) merupakan jenis virus baru yang ditemukan pada manusia di propinsi Wuhan, Cina pada bulan Desember 2019. Virus ini dapat menular dari manusia ke manusia melalui tetesan kecil (droplet) dari hidung atau mulut pada saat batuk, bersin, atau berbicara. Oleh karena itu, di masa pandemi ini sangat penting untuk menjaga jarak dengan orang lain dan menghindari wilayah dengan persebaran COVID-19 yang tinggi. Pada penelitian ini dilakukan klasterisasi persebaran virus Corona di DKI Jakarta dengan menerapkan metode data mining. Pengelompokan dilakukan berdasarkan parameter jumlah ODP, PDP, kasus Positif, pasien sembuh dan pasien meninggal. Pada penelitian ini, untuk melakukan klasterisasi data digunakan metode K-Means dan metode pengukuran jarak Euclidean. Penelitian ini menghasilkan prototipe aplikasi pengelompokan data persebaran pasien Covid-19. Berdasarkan pengujian, jumlah klaster yang direkomendasikan adalah 9 klaster. Hasil penelitian ini diharapkan dapat membantu pemerintah DKI Jakarta dalam mengambil keputusan strategis dalam mengurangi persebaran virus Corona di DKI Jakarta.Kata kunci: corona, Jakarta, klasterisasi, k-means Abstract[Corona Virus (Covid-19) Clustering in Jakarta using K-Means Method] Coronavirus (COVID-19) is a new type of virus found in humans in the province of Wuhan, China in December 2019. This virus can be transmitted from person to person through small droplets from the nose or mouth when coughing, sneezing, or talking. Therefore, during this pandemic, it is very important to keep your distance from other people and avoid areas with a high spread of COVID-19 In this study, the distribution of the Coronavirus in DKI Jakarta was clustered by applying the data mining method. The clustering was carried out based on the parameters of the number of ODP, PDP, positive cases, patients recovered and patients died. In this study, to perform data clustering, the K-Means method, and the Euclidean distance measurement method were used. This study produced a prototype application for the distribution of Covid-19 patient distribution data. Based on the test, the recommended number of clusters is 9 clusters. The results of this study are expected to help the DKI Jakarta government in making strategic decisions in reducing the spread of the Coronavirus in DKI Jakarta.Keywords: corona, Jakarta, clustering, k-means
 
Publisher Universitas Darussalam Gontor
 
Contributor
 
Date 2020-10-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/4905
10.21111/fij.v5i2.4905
 
Source Fountain of Informatics Journal; Vol 5, No 2 (2020): November; 52-59
2548-5113
2541-4313
 
Language eng
 
Relation https://ejournal.unida.gontor.ac.id/index.php/FIJ/article/view/4905/pdf_43
 
Rights Copyright (c) 2020 Fountain of Informatics Journal
http://creativecommons.org/licenses/by-nc-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library