CLUSTERING RUMAH ISOLASI DI KOTA SURAKARTA
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Nov 27, 2021
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Abstract
This study aims to conduct clustering of isolation houses for residents of the city of Surakarta who carry out self-isolation due to Covid-19 infection. The isolation house was processed with the k-means algorithm for subsequent analysis. The data collection time span is 1 day for the emergency PPKM period. The data is clustered with the R programming language which is open source. The k-means algorithm evaluates the distance between data based on the degree of similarity to the centroid. This independently working algorithm produces data visualization of clustering isolation houses for residents of the city of Surakarta who are self-isolating because they are infected with Covid-19, namely the optimal value of clusters in 54 villages, namely c1 as many as 2 villages, c2 = 3 villages, c3 = 8 villages and c4 = 41. Ward. The results of clustering based on these data show that the city of Surakarta has 2 (3%) red zones, 3 (5%) orange zones and 8 (14%) yellow zones and 41 (75%) green zones. This shows that the use of residents' houses which have been converted as self-isolation facilities and efforts to limit community activities can control the spread of Covid-19 in the city of Surakarta.
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Rochmadi, T. (2021). CLUSTERING RUMAH ISOLASI DI KOTA SURAKARTA. Electro Luceat, 7(2), 118-131. https://doi.org/10.32531/jelekn.v7i2.400
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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[5] D. T. Larose, DATA MINING METHODS AND MODELS. John Wiley & Sons, Inc, 2006.
[6] S. Dawaty, “K-Means Clustering,” Univeristas Raharja, 2020. .
[7] T. K. P. G. T. Nasional, “Pengelompokan Kriteria Risiko COVID-19 di Daerah Berdasarkan Zonasi Warna,” BNPB, 2020. .
[8] H. Hasanah, Nurmalitasari;, and N. A. Sudibyo, “Implemenetasi Data Mining Clustering untuk Mengetahui Potensi Produktifitas Kacang Tanah di Indonesia,” NJCA (National J. Comput. Its Appl., vol. 5, no. 1, pp. 1–7, 2020.
[9] N. A. Sudibyo, A. Iswardani, K. Sari, and S. Suprihatiningsih, “Penerapan Data Mining Pada Jumlah Penduduk,” Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 1, no. 3, pp. 199–207, 2020.
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[13] E. Prasetyo, Data Mining Konsep dan Aplikasi Menggunakan Matlab. ANDI Offset, 2012.
[14] D. A. I. C. Dewi and D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” Matrix J. Manaj. Teknol. dan Inform., vol. 9, no. 3, pp. 102–109, 2019, doi: 10.31940/matrix.v9i3.1662.
[15] J. Jia et al., “Epidemiological Characteristics on the Clustering Nature of COVID-19 in Qingdao City, 2020: A Descriptive Analysis,” Disaster Med. Public Health Prep., vol. 14, no. 5, pp. 643–647, 2020, doi: 10.1017/dmp.2020.59.
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[17] Kementerian Sosial Republik Indonesia, Panduan Penyiapan Fasilitas Shelter untuk karantina dan isolasi terkait COVID-19 Berbasis Komunitas. Kementerian Sosial Republik Indonesia, 2020.