Khoirushidqi, Ahmad Gymnastiar (2022) PERBANDINGAN ALGORITMA K-MEANS DAN K MEDOIDS UNTUK PENGELOMPOKKAN DATA COVID-19 DI INDONESIA. Sarjana thesis, Universitas Siliwangi.
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01. COVER.pdf Download (221kB) |
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02. ABSTRAK.pdf Download (205kB) |
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03. LEMBAR PENGESAHAN PEMBIMBING.pdf Download (80kB) |
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04. LEMBAR PENGESAHAN PENGUJI.pdf Download (74kB) |
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05. LEMBAR PERNYATAAN KEASLIAN.pdf Download (71kB) |
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06. KATA PENGANTAR.pdf Download (306kB) |
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07. DAFTAR ISI.pdf Download (410kB) |
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08. DAFTAR TABEL.pdf Download (1MB) |
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09. DAFTAR GAMBAR.pdf Download (204kB) |
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10. BAB I.pdf Download (322kB) |
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11. BAB II.pdf Download (576kB) |
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15. DAFTAR PUSTAKA.pdf Download (431kB) |
Abstract
The COVID-19 pandemic has become a serious challenge to public health in Indonesia. In an effort to better understand the dynamics of virus spread, researchers used k-means and k-medoids data grouping methods to analyze COVID-19 data in Indonesia. First, collecting COVID-19 epidemiological data, including the number of positive cases, cure rates, and number of deaths, from various regions in Indonesia. Next, it uses the k-means method to group these regions based on similar epidemiological characteristics, such as case spread rate and severity. In addition, we also apply the k-medoids method to group areas based on more local patterns of case spread and focus on the actual central points of each group. This analysis allows us to identify patterns of virus spread that may occur in various regions in Indonesia, as well as provide valuable information for policymakers in determining appropriate response strategies. Keywords: Covid-19; Clustering; K-Means; K-Medoids; Data Analysis
| Item Type: | Thesis (Sarjana) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Informatika |
| Depositing User: | Lelis Masridah |
| Date Deposited: | 02 Sep 2024 07:46 |
| Last Modified: | 02 Sep 2024 07:46 |
| URI: | http://repositori.unsil.ac.id/id/eprint/13923 |
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