Klasterisasi Wilayah Prioritas Vaksin Menggunakan Algoritma K-Means Clustering
DOI:
https://doi.org/10.30865/klik.v4i3.1334Keywords:
Covid; K-Means Clustering; Vaccines; North SumatraAbstract
K-means clustering algorithm is a clustering method which is done by partition (partitional clustering). The use of clusters intends to partition a number of objects into groups where each object is into the closest group so that it will produce groups with significant differences. In addition, efforts have been made by the government to prevent wider disease transmission, among others, by implementing large-scale social restrictions and monitoring areas where there is a lot of migration of local and foreign residents as well as vaccinating. Looking at the data on the number of people who were infected, died from the Covid-19 virus until they recovered, which occurred in various regions in Indonesia. For this reason, it is necessary to cluster the area from the red, yellow, and green zones, which means for the red zone itself, it means that the area is a danger area and an area with a large number of viruses infected. This study aims to solve the problem, namely to produce applications that can provide information about priority areas for vaccines in North Sumatra, clustering the North Sumatra area, knowing vaccine priority areas using the K- Means algorithm. To find out the results of using the K-Means algorithm application, namely the application can classify Covid 19 cases in each Regency/City in North Sumatra Province into clusters of C1 (High), C2 (Medium) and C3 (Low). Based on the results of the clustering, Medan is in the C1 cluster with a distance value of 0.00 so that it can be prioritized for covid 19 vaccination activities. In the C2 (Medium) clustering there is one Regency, namely Deli Serdang with a 4th iteration value with a distance of 0.00. In the C3 (Low) cluster. Covid-19 case data from march-November 2021 in Nort Sumatra Province was calculated in 4 iterations until there there were no more data changes in the clustering process.
Downloads
References
D. T. Utari, “Analisis Karakteristik Wilayah Transmisi Covid-19 Dengan Menggunakan Metode K-Means Clustering,” J. Media Tek. Dan Sist. Ind., vol. 5, no. 1, pp. 25–32, 2021.
T. S. Pratiwi and P. T. Arisanto, “Kolaborasi Pemerintah dan Masyarakat Sipil Merespon Covid-19 di Daerah Istimewa Yogyakarta Melalui Pendekatan Crisis Visualizations,” in Proceeding International Relations on Indonesian Foreign Policy Conference, 2021, pp. 249–271.
A. H. Ardiansyah, W. Nugroho, N. H. Alfiyah, R. A. Handoko, and M. A. Bakhtiar, “Penerapan Data Mining Menggunakan Metode Clustering untuk Menentukan Status Provinsi di Indonesia 2020,” in Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 2020, pp. 329–333.
T. Covid and P. IDI, “standar perlindungan dokter di era Covid-19,” 2020.
T. M. Tambunan, M. Sulistiono, and E. Rasywir, “Penerapan Data Mining Menggunakan Metode K-Means Untuk Menentukan Status Covid-19 di Jambi,” J. Manaj. Teknol. Dan Sist. Inf., vol. 2, no. 2, pp. 196–204, 2022.
R. Gumilar, R. Modjo, R. S. Putra, and D. L. Setyowati, “Implementasi Pencegahan Dan Pengendalian Covid-19 Di Fasilitas Pelayanan Kesehatan Di Wilayah Provinsi Kalimatan Timur,” PREPOTIF J. Kesehat. Masy., vol. 5, no. 2, pp. 1084–1096, 2021.
A. Aditya, I. Jovian, and B. N. Sari, “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019,” J. Media Inform. Budidarma, vol. 4, no. 1, pp. 51–58, 2020.
I. G. T. Isa, F. Elfaladonna, and I. Ariyanti, Buku Ajar Sistem Pendukung Keputusan. Penerbit NEM, 2022.
N. Dwitri, J. A. Tampubolon, S. Prayoga, F. I. R. H. Zer, and D. Hartama, “Penerapan algoritma K-Means dalam menentukan tingkat penyebaran pandemi COVID-19 di Indonesia,” J. Teknol. Inf., vol. 4, no. 1, pp. 128–132, 2020.
R. Sofiah, S. Suhartono, and R. Hidayah, “Analisis karakteristik sains teknologi masyarakat (STM) sebagai model pembelajaran: sebuah studi literatur,” Pedagog. J. Penelit. Pendidik., vol. 7, no. 1, 2020.
N. Arifin, R. H. Irawan, and I. N. Farida, “Algoritma K-Means Untuk Memprediksi Stok Bahan Baku Produksi.” Universitas Nusantara PGRI Kediri, 2022.
R. A. Fadhallah, Wawancara. Unj Press, 2021.
R. Agustianti et al., Metode Penelitian Kuantitatif Dan Kualitatif. Tohar Media, 2022.
T. D. Rosmalasari, M. A. Lestari, F. Dewantoro, and E. Russel, “Pengembangan E-Marketing Sebagai Sistem Informasi Layanan Pelanggan Pada Mega Florist Bandar Lampung,” J. Soc. Sci. Technol. Community Serv., vol. 1, no. 1, pp. 27–32, 2020.
A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” J. Tekno Kompak, vol. 15, no. 2, pp. 25–36, 2021.
A. Nurrachman, “Analisis perbandingan Cluster K-Means dan Fuzzy C-Means hasil ujian Nasional SMP.” Universitas Islam Negeri Maulana Malik Ibrahim, 2021.
M. A. AZRA N, “PENGGUNAAN DAVIES BOULDIN INDEX DALAM PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS UNTUK KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN TINGKAT INDIKATOR KETENAGAKERJAAN.” Matematika, 2023.
M. Z. Nasution and M. S. Hasibuan, “Pendekatan Initial Centroid Search Untuk Meningkatkan Efisiensi Iterasi Klustering K-Means,” Techno. Com, vol. 19, no. 4, pp. 341–352, 2020.
F. Yunita, “Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru,” Sist. J. Sist. Inf., vol. 7, no. 3, pp. 238–249, 2018.
R. K. Dinata, H. Novriando, N. Hasdyna, and S. Retno, “Reduksi atribut menggunakan information gain untuk optimasi cluster algoritma k-means,” J. Edukasi dan Penelit. Inf., vol. 6, no. 1, pp. 48–53, 2020.
T. Ginting, D. L. Kaban, and R. Ginting, “Kepatuhan pedagang pasar pagi dalam melaksanakan protokol kesehatan pencegahan COVID-19,” J. Prima Med. Sains, vol. 3, no. 1, pp. 6–12, 2021.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasterisasi Wilayah Prioritas Vaksin Menggunakan Algoritma K-Means Clustering
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Rahmat Kurniawan, Muhammad Siddik Hasibuan, Riska Hasibuan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
