Vol. 4 No. 3 (2020), Articles
Vol. 4 No. 3 (2020)

Penerapan Data Mining Menggunakan Metode Clustering untuk Menentukan Status Provinsi di Indonesia 2020

Articles
Published 2020-08-21
Ahmad Husain Ardiansyah
Universitas Muhammadiyah Magelang
Wisnu Nugroho
Universitas Muhammadiyah Magelang
Nurul Hanifatul Alfiyah
Universitas Muhammadiyah Magelang
Rahmat Aji Handoko
Universitas Muhammadiyah Magelang
Muhammad Arfan Bakhtiar
Universitas Muhammadiyah Magelang
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Keywords

Covid-19
Disease
K-Means Clustering

How to Cite

Penerapan Data Mining Menggunakan Metode Clustering untuk Menentukan Status Provinsi di Indonesia 2020. (2020). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 4(3), 329-333. https://doi.org/10.29407/inotek.v4i3.108
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Abstract

Coronavirus is a group of viruses that can cause disease in animals or humans. Several types of coronaviruses are known to cause respiratory infections in humans ranging from cold coughs to more serious ones such as Middle East Respiratory Syndrome (MERS)  and Severe Acute Respiratory Syndrome (SARS). A new type of Coronavirus has been found to cause COVID-19. In Indonesia alone, this virus began to spread from the beginning of March until now. Many areas affected by this virus. There are also many regions  where the level of outbreaks of this virus tends to be small. The choice of clustering method in the classification process to divide regions in Indonesia to be divided into red or yellow or green zones is based on calculations using the K -Mean clustering  formula. KMeans Clustering algorithm is an algorithm that groups the same data in a certain group and different data in another group. The results of the grouping of regions included in the red zone are Jakarta with a number of more than 7,000 positive patients infected, for those entering the yellow zone namely Banten with more than 800 positive patients infected. And as many as less than 600 infected patients can be categorized as green zones.

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References

Pertanyaan jawaban terkait COVID-19 untuk publik. (n.d.).

Saleh, A. (2015). Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. 2(3), 207–217.

Method, I., Based, K. C., Value, S., Interface, W., Study, C., & Magelang, I. U. M. M. (2015). Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang. 18(1), 76–82.

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