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Donny Avianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Journal : Jurnal Teknologi Terapan

 1 
IMPLEMENTASI ALGORITMA K-MEDOIDS UNTUK CLUSTERING WILAYAH TERINFEKSI KASUS COVID-19 DI DKI JAKARTA Muh Arifandi; Arief Hermawan; Donny Avianto
Jurnal Teknologi Terapan Vol 7, No 2 (2021): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v7i2.353

Abstract

In early March 2019, Indonesia was hit by the Covid-19 (Corona) outbreak. The increase in the number of patients infected with the Covid-19 virus is increasing day by day and is already difficult to control. Jakarta is no exception. To prevent the increase in cases of COVID-19, it is necessary to create a cluster or grouping of certain areas (Urban village) based on the number of positive, treated, recovered, died and isolated. This grouping will assist the DKI Jakarta government in providing appropriate handling according to the Urban village pattern. The data that will be used as a research study is the data on the distribution of the status of infected cases of Covid-19 in DKI Jakarta Province on May 20, 2021. The K-Medoids algorithm is a method that can determine a set of clusters among a group of data that is close to an object. Based on the research studies that have been carried out, it can be concluded that in the data mining technique, the total grouping of Covid-19 infected cases based on urban areas in DKI Jakarta Province uses the k-medoids algorithm with three clusters. Cluster 0, cluster 1, cluster 2. The highest Covid-19 infected cases in DKI Jakarta Province are shown in cluster 3 with 31 regions. The results of this grouping research will assist the DKI Jakarta government in providing appropriate handling according to the Urban village pattern. K-Medoids can be implemented using large amounts of data with complex attributes.
Co-Authors Adhitama, Satriya Adicahya, Bina Sukma Adityo Permana Wibowo Alfin Syarifuddin Syahab Alwani, Adie G. Amalia Rizki Wulandari Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Budiyanto Arief Hermawan Arieska Restu Harpian Dwika Arif Hermawan, Arif Ashari, Nadia Aziz Perdana Baiq Nurul Azmi Bowo Hirwono Budiyanto, Irfan Budiyanto, Irffan Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dwi Ratnawati, Dwi Edi Priyanto Enggar Novianto Enggar Novianto Erfin Nur Rohma Khakim Fadhila, Arifa Farras Fadilah, Faiz Fahri Putra Herlambang Fakharudin, Panji Rangga Adzan Fajar Faqih, Allan Bil Febiansyah Annaufal Ahnaf Fauzi Ferdinandus Edwin Penalun Gumilang, Muhammad Satrio Gunawan, Asrul Hanif, Rifqi Fadhlurrahman Hardiyantari, Oktavia Herdy Andriksen Ilmy Eka Handayani Imantoko Imantoko Iqbal, Muhammad Izza Jagad Raya Ramadhan Kusban, Muhammad Kusumastuti, Asriana Dyah Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Rizki Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Nazar Iqbal Bimantoro Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Putra, Kristianto Pratama Dessan Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Rizarta, Rusma Eko Fiddy Rizky Samudra Falasyfa Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Setiawan, Muhhamad Ajun Siti Rokhanah Soraya Fatmawati Sri Wulandari SRI WULANDARI Sutarman Sutarman Syahab, Alfin Syarifuddin Tri Untoro, Iwan Hartadi Tri Widodo Vivianti Wahid, Ach. Nur Aqil Widyastuti, Evi