Penerapan Algoritma K-Medoids Clustering Dalam Pembentukan Zona Cluster Vaksin Boster
-
Siti Lialiyah
*
STMIK Widya Cipta Dharma, Samarinda,
Indonesia
- Reza Andrea Politeknik Pertanian Negeri Samarinda, Samarinda, Indonesia (*) Corresponding Author
Abstract
The effects of the COVID-19 virus pandemic are quite bad for people's lives both in Indonesia and especially in North Sumatra. The spread of the virus is quite fast from the interaction of every community, causing the government to make policies to limit the activities of each community. In addition to policies in limiting community activities, the government also makes policies by distributing vaccines for free to every community starting from the first vaccine, the second and last vaccine is the third vaccine (booster). The purpose of the vaccine itself is to stimulate the body's antibodies to recognize the weakened virus in the vaccine. The aim of the vaccine is to slow the spread of the virus itself. The third vaccine (booster) is a complementary vaccine given by the government so that antibodies can completely inhibit a person from being affected by the COVID-19 virus. Therefore, it is necessary to accelerate the process of administering the third vaccine (booster). This can be done by forming clusters in each region. The purpose of forming clusters is to be able to identify priority areas that should be given the third vaccine (booster). Therefore we need a technique that is able to group/cluster the third vaccine administration zone (booster). One technique that can be used is the K-Medoids Algorithm. The expected results of the research using the K-Medoids Algorithm are able to form a cluster zone which will later be able to find out which areas are the priority for giving the third vaccine (booster).
Downloads
References
Y. F. S. Y. Damanik, S. Sumarno, I. Gunawan, D. Hartama, and I. O. Kirana, “Penerapan Data Mining Untuk Pengelompokan Penyebaran Covid-19 Di Kalimantan Timur Menggunakan Algoritma K-Means,” J. Ilmu Komput. dan Inform., vol. 1, no. 2, pp. 109–132, 2021, doi: 10.54082/jiki.13.
D. P. Sari, “Implementasi Algoritma K-Means Dalam Menentukan Tingkat Penyebaran Pandemi Covid-19 Di Sumatera Barat,” Comput. Based Inf. Syst. J., vol. 9, no. 1, pp. 50–56, 2021, doi: 10.33884/cbis.v9i1.3646.
N. Mirantika, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Penyebaran Covid-19 di Provinsi Jawa Barat,” Nuansa Inform., vol. 15, no. 2, pp. 92–98, 2021, doi: 10.25134/nuansa.v15i2.4321.
Alvina Felicia Watratan, Arwini Puspita. B, and Dikwan Moeis, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,” J. Appl. Comput. Sci. Technol., vol. 1, no. 1, pp. 7–14, 2020, doi: 10.52158/jacost.v1i1.9.
K. K. R. INDONESIA, “Vaksinasi COVID-19 Nasional,” INDONESIA, KEMENTERIAN KESEHATAN REPUBLIK, 2022. https://vaksin.kemkes.go.id/#/vaccines.
C.-19 Sumut, “Kasus Covid-19 Di SUMUT,” Pemprov Sumut, 2022. https://covid19.sumutprov.go.id/article/title/perkembangan-kasus-covid19-tanggal-12-april-2022-di-provinsi-sumatera-utara.
D. P. Utomo, P. Sirait, and R. Yunis, “Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5. 0,” Media Inform. Budidarma, vol. 4, no. 4, pp. 994–1006, 2020.
D. P. Utomo and S. Aripin, “Penerapan Algoritma C5 . 0 Untuk Mengetahui Pola Kepuasan Mahasiswa di Masa Pembelajaran Daring,” in Seminar Nasional Riset Dan Information Science (SENARIS), 2021, vol. 3, pp. 7–12.
E. Buulolo, R. Syahputra, and A. Fau, “Algoritma K-Medoids Untuk Menentukan Calon Mahasiswa Yang Layak Mendapatkan Beasiswa Bidikmisi di Universitas Budi Darma,” J. Media Inform. Budidarma, vol. 4, no. 3, p. 797, 2020, doi: 10.30865/mib.v4i3.2240.
N. Pulungan, S. Suhada, and D. Suhendro, “Penerapan Algoritma K-Medoids Untuk Mengelompokkan Penduduk 15 Tahun Keatas Menurut Lapangan Pekerjaan Utama,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 329–334, 2019, doi: 10.30865/komik.v3i1.1609.
S. Darma and G. W. Nurcahyo, “Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids,” J. Inform. Ekon. Bisnis, vol. 3, pp. 89–94, 2021, doi: 10.37034/infeb.v3i3.87.
R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 109–116, 2021, doi: 10.30871/jaic.v5i1.3237.
E. Buulolo and B. Purba, “Algoritma Clustering Untuk Membentuk Cluster Zona,” vol. 19, pp. 59–67.
D. Marlina, N. Lina, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 4, no. 2, p. 64, 2018, doi: 10.24014/coreit.v4i2.4498.
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.
N. L. Anggreini, “Teknik Clustering Dengan Algoritma K-Medoids Untuk Menangani Strategi Promosi Di Politeknik Tedc Bandung,” J. Teknol. Inf. dan Pendidik., vol. 12, no. 2, pp. 1–7, 2019, doi: 10.24036/tip.v12i2.215.
S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. I. R.H.Zer, and D. Hartama, “Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia,” J. Teknol. Inf., vol. 4, no. 1, pp. 166–173, 2020, doi: 10.36294/jurti.v4i1.1296.
F. Telaumbanua, J. M. Purba, and D. P. Utomo, “Analysis of Online Learning Understanding Patterns at Budi Darma University Using the C5 . 0 Algorithm,” vol. 5, no. 2, pp. 118–122, 2021, doi: 10.30865/ijics.v5i2.3129.
D. P. Utomo and Mesran, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,” Media Inform. Budidarma, vol. 4, no. 2, pp. 437–444, 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma K-Medoids Clustering Dalam Pembentukan Zona Cluster Vaksin Boster
Pages: 124−129
Copyright (c) 2022 Siti Lialiyah, Reza Andrea
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).
