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All Journal Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Muhammad Arfan Bakhtiar
Universitas Muhammadiyah Magelang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Penerapan Data Mining Menggunakan Metode Clustering untuk Menentukan Status Provinsi di Indonesia 2020 Ahmad Husain Ardiansyah; Wisnu Nugroho; Nurul Hanifatul Alfiyah; Rahmat Aji Handoko; Muhammad Arfan Bakhtiar
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 4 No. 3 (2020): PROSIDING SEMNAS INOTEK Ke-IV Tahun 2020
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v4i3.108

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.
Co-Authors Ahmad Husain Ardiansyah Nurul Hanifatul Alfiyah Rahmat Aji Handoko Wisnu Nugroho