Comparison of K-Means and K-Medoids Algorithms for Clustering the Spread of the COVID-19 Outbreak in Indonesia


Wargijono Utomo(1*);

(1) Universitas Krisnadwipayana
(*) Corresponding Author

  

Abstract


The spread of Corono Virus 19 is very fast through effective human-to-human transmission through close contact and respiratory droplets such as coughing or sneezing. Various studies have been conducted to deal with COVID 19, but until now it has not been found how to stop the spread of this virus. Based on data obtained from the covid19.go.id page accessed on January 1, 2021 which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of cases confirmed, died 29,998 or 2.8% of the confirmed cases. In this study, comparing the two algorithms in the dataset which aims to analyze grouping patterns and determine the best method of data processing. The data used comes from the Ministry of Health, there are 4 attributes including confirmed cases, treatment, recovery and death, in this study only 2 attributes are used, namely confirmed cases and death.  From the results of data analysis and processing through a comparison between the K-Means method and the K-Medoids for grouping the spread of the corona 19 virus in Indonesia, with the Davies Boulden index value from the K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-value. 5 is 0.064, while K-Medoids at the k-2 value is 0.411. Thus, from the two methods used, it can be found that the best method for clustering the spread of the corona 19 virus outbreak in Indonesia is the K-Means method.


Keywords


COVID-19; Clustering; K-Means; K-Medoids

  
     

Article Metrics

Abstract view: 16 times
     

Digital Object Identifier

  

Cite

References


Muhammad Adnan Shereen, Suliman Khan, Abeer Kazmi, Nadia Bashir, Rabeea Siddique, “COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses,” Journal of Advanced Research 24 (2020) 91–98

Nayuni Dwitri dkk, “Penerapan Algoritma K-Means dalam Menentukan Tingkat Penyebaran Pandemi Covid-19 di Indonesia,” Jurnal Teknologi Informasi, Vol. 4, No.1, Juni 2020

Ahmed Hamed et al, “Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm, “Research Square 2020

M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder, Priya Podder, “Data analytics for novel coronavirus disease,“ Informatics in Medicine Unlocked 20 (2020), 100374.

Ahmed T. Sahlol et al, “COVID‑19 image classiication using deep features and fractional‑order marine predators algorithm,” Scientific Report, 10:15364 https://doi.org/10.1038/s41598-020-71294-2

R. A. Indraputra , R. Fitriana, “K-Means Clustering Data COVID-19,” Jurnal Teknik Industri, Volume 10 No.3. Desember 2020.

Achmad Solichin, Khansa Khairunnisa, “Klasterisasi Persebaran Virus Corona (Covid-19) Di DKI Jakarta Menggunakan Metode K-Means,” Fountain of Informatics Journal Volume 5, No. 2, November 2020

Frenda Farahdinna, Irfan Nurdiansyah, Apriati Suryani, Arief Wibowo, “perbandingan algoritma k-means dan k-medoids dalam klasterisasi produk asuransi perusahaan nasional,” Jurnal Ilmiah FIFO, Volume 11 No. 2, November 2019.

Iin Parlina, Agus Perdana Windarto, Anjar Wanto, M.Ridwan Lubis, “memanfaatkan algoritma k-means dalam menentukan pegawai yang layak mengikuti asessment center untuk clustering program sdp,” CESS (Journal of Computer Engineering System and Science), Volume 3, No.1, Januari 2018.

Prima Resti Nastiti, Arief Bramanto Wicaksono Putra, “perbandingan algoritma k-means dan fuzzy c-means clustering untuk kualifikasi data kinerja dosen di jurusan teknologi informasi polnes,” PROSIDING SNSebatik 2017, Vol 1 No. 1, Juni 2017.

Nova Agustina, Prihandoko, 2020, Perbandingan Algoritma K-Means Dengan Algoritma Fuzzy C-Means Untuk Clustering Tingkat Kedisiplinan Kinerja Karyawan, Jurnal RESTI, Volume 2 No.3 (2018), 621-626.

Insanul Kamila, Ulya Khairunnisa, Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, Vol. 5, No. 1, Hal. 119-125, Februari 2019.

N. L. Anggraeni, “Teknik Clustering Dengan Algoritma K-Medoids Untuk Menangani Strategi Promosi Di Politeknik TEDC Bandung,” Jurnal Teknologi Informasi dan Pendidikan, vol. 12 no. 2 pp. 1-7, 2019.

Samudi, Slamet Widodo, Herlambang Brawijaya,2020, The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic, Jurnal dan Penelitian Teknik Informatika Volume 5, Number 1, Oktober 2020

Iqbal Dzulfiqar Iskandar, Melisa Winda Pertiwi, Mira Kusmira, Imam Amirulloh, “komparasi algoritma clustering data media online pada proses bisnis,” Jurnal IKRA-ITH Informatika Vol. 2 No. 3, November 2018.

R. D. Ramadhani and D. J. Ak, “Evaluasi K-Means dan K-Medoids pada Dataset Kecil,” Semin. Nas.

Inform. dan Apl., no. September, pp. 20–24, 2017.

H. Zayuka, S. M. Nasution, and Y. Purwanto, “Perancangan Dan Analisis Clustering Data Menggunakan Metode K-Medoids Untuk Berita Berbahasa Inggris Design and Analysis of Data Clustering Using Kmedoids Method For English News,” e-Proceeding Eng., vol. 4, no. 2, pp. 2182–2190, 2017

A F Khairatia , A A Adlinaa , G F Hertonoa , B D Handaria, “Kajian Indeks Validitas pada Algoritma K-Means Enhanced dan K-Means MMCA,” Prosiding Seminar Nasional Matematika, PRISMA 2 (2019): 161-170.

Rachmah Dewi Kusumah, Budi Warsito, Moch. Abdul Mukid, “perbandingan metode k–means dan self organizing map (studi kasus: pengelompokan kabupaten/kota di jawa tengah berdasarkan indikator indeks pembangunan manusia 2015),” JURNAL GAUSSIAN, Volume 6, Nomor 3, Tahun 2017, Halaman 429-437.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Wargijono Utomo

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 ILKOM Jurnal Ilmiah indexed by

doaj_logoCROSSREF_logoROAD_logoPKP_Index_logoGoogle_Scholar_logosinta_logogaruda_logoonesearch_logoBASE_logoWordcat_logo

___________________________________________________________
ILKOM Jurnal Ilmiah
ISSN 2548-7779
Published by Teknik Informatika Fakultas Ilmu Komputer Universitas Muslim Indonesia
W : https://fikom.umi.ac.id/
E : jurnal.ilkom@umi.ac.id

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0