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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Riset Informatika Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer INTI Nusa Mandiri Profitabilitas Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknik Komputer dan Informatika (JTeKI)
Zulia Imami Alfianti
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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Journal : Jurnal Mantik

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Grouping of Covid-19 Affected Areas in Bogor City Using The K-Means Algorithm Zulia Imami Alfianti; Sugiono; Mochammad Abdul Azis; Ahmad Fauzi
Jurnal Mantik Vol. 4 No. 4 (2021): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2021.1142.pp2336-2341

Abstract

Clustering plays an important role in processing big data, making predictions and overcoming anomalies in data, identical characteristics in data sets are grouped using iterative techniques. Because data is always evolving from day to day, very large data sets with little can be identified into interesting patterns by grouping, special methods are needed to handle it. In December 2019 there was an outbreak of acute respiratory syndrome caused by coronavirus 2 infection that occurred in Wuhan and on February 12, 2020, the World Health Organization officially named the disease Corona Virus 2019 (Covid 19). This research will conduct clustering of areas affected by Covid 19 in the City of Bogor. The clustering was done using the K-Means method and dividing the data into 3 clusters, namely the low-impact cluster, the medium-impact cluster and the high-impact cluster. The results showed that from 68 urban villages in the city of Bogor, 45% of the area was in the low-affected category, 35.29% of the area was in the medium-affected category and 19.12% of the area was in the high-affected category.
Co-Authors Ahmad Fauzi Ahmad Fikri Amin Akrom, Akrom Dede Mustomi Deni Gunawan Eko Yulianto Fauzi , Ahmad Fauzi Ahmad Muda Ginabila Ginabila Ginabila, Ginabila KOMALASARI, YULI Laetussaadah, Lela Mochammad Abdul Azis Pratiwi, Risca Lusiana Siti Fauziah Sugiono