Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Kasus Covid-19 di Dunia
DOI:
https://doi.org/10.24014/sitekin.v18i2.12469Abstract
Pada tanggal 16 Maret 2020 telah dikonfirmasi terdapat lebih dari 180.000 kasus Covid-19 di seluruh dunia, dengan lebih dari 7.000 kematian. Berbagai upaya dilakukan oleh negara-negara yang terdampak oleh Covid-19 untuk mengatasi penyebaran virus ini. Terdapat negara yang memiliki tingkat kesembuhan dan pemulihan yang tinggi, sebaliknya juga terdapat negara yang kesulitan dalam penanganan pandemi Covid-19 ini. Penelitian dilakukan untuk mengelompokkan negara-negara yang memiliki pola kasus Covid-19 di dunia. Kedepannya, hasil pengelompokan dapat dijadikan acuan dan pola gambaran negara yang memiliki tingkat pemulihan rendah dapat mengamati proses pemulihan negara yang memiliki tingkat pemulihan tinggi yang berada dalam kelompoknya. Untuk melakukan klasterisasi pada penelitian ini menggunakan algoritma DBSCAN dan K-Means. Setelah melalui beberapa percobaan diperoleh hasil bahwa K-Means lebih unggul daripada DBSCAN dalam mengelompokkan kasus Covid-19. Algoritma K-Means memiliki nilai SI terbaik sebesar 0,6902 yang terletak pada percobaan dengan nilai k = 8.
References
A. Bénassy-Quéré and B. Weder di Mauro, “Europe in the time of Covid-19: A new crash test and a new opportunity,” Eur. Time Covid-19, p. 1, 2020.
P. Dashraath et al., “Coronavirus disease 2019 (COVID-19) pandemic and pregnancy,” Am. J. Obstet. Gynecol., vol. 222, no. 6, pp. 521–531, 2020, doi: 10.1016/j.ajog.2020.03.021.
H. Qi et al., “COVID-19 transmission in Mainland China is associated with temperature and humidity: a time-series analysis,” Sci. Total Environ., vol. 728, p. 138778, 2020.
M. Her, “Repurposing and Reshaping of Hospitals During the COVID-19 Outbreak in South Korea,” One Heal., vol. 10, no. 1, pp. 1–3, 2020, doi: 10.1016/j.onehlt.2020.100137.
A. C. Benabdellah, A. Benghabrit, and I. Bouhaddou, “A survey of clustering algorithms for an industrial context,” Procedia Comput. Sci., vol. 148, pp. 291–302, 2019, doi: 10.1016/j.procs.2019.01.022.
I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, p. 119, 2019, doi: 10.24014/rmsi.v5i1.7381.
Mustakim et al., “DBSCAN algorithm: Twitter text clustering of trend topic pilkada pekanbaru,” J. Phys. Conf. Ser., vol. 1363, no. 1, 2019, doi: 10.1088/1742-6596/1363/1/012001.
Mustakim, M. Z. Fauzi, Mustafa, A. Abdullah, and Rohayati, “Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms,” J. Phys. Conf. Ser., vol. 1783, no. 1, p. 012016, 2021, doi: 10.1088/1742-6596/1783/1/012016.
B. S. Ashari, S. C. Otniel, and R. Rianto, “Perbandingan Kinerja K-Means dengan DBSCAN untuk Metode Clustering data Penjualan Online Retail,” J. Siliwangi Seri Sains dan Teknol., vol. 5, no. 2, pp. 64–67, 2019.
A. Malav, K. Kadam, and P. Kamat, “Prediction of heart disease using kmeans and artificial neural network as hybrid approach to improve accuracy,” Int. J. Eng. Technol., vol. 9, no. 4, pp. 3081–3085, 2017.
W. Jing, C. Zhao, and C. Jiang, “An improvement method of DBSCAN algorithm on cloud computing,” Procedia Comput. Sci., vol. 147, pp. 596–604, 2019, doi: 10.1016/j.procs.2019.01.208.
B. M. Metisen and H. L. Sari, “Analisis Clustering Menggunakan Metode K-Means Dalam Pengelompokkan Penjualan Produk Pada Swalayan Fadhila,” J. Media Infotama, vol. 11, no. 2, pp. 110-118. ISSN: 1858 – 2680, 2015.
M. Pietrzykowski, “Comparison of mini-models based on various clustering algorithms,” Procedia Comput. Sci., vol. 176, pp. 3563–3570, 2020, doi: 10.1016/j.procs.2020.09.030.
Rahmawati and M. Faisal, “Analisis Cluster untuk Pengelompokan Desa Berdasarkan Indikator Penyakit Diare,” Saintifik, vol. 5, no. 1, pp. 75–80, 2019.
I. D. Iskandar, M. W. Pertiwi, M. Kusmira, and I. Amirulloh, “Komparasi Algoritma Clustering Data Media Online,” J. IKRA-ITH Inform., vol. 2, no. 4, pp. 1–8, 2018.
M. A. Nahdliyah, T. Widiharih, and A. Prahutama, “Metode K-Medoids Clustering dengan Validasi Silhouette Index dan C-Index,” vol. 8, no. 2, pp. 161–170, 2019.
X. Wang and Y. Xu, “An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index,” IOP Conf. Ser. Mater. Sci. Eng., vol. 569, no. 5, 2019, doi: 10.1088/1757-899X/569/5/052024.
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