The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic

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Samudi Samudi Slamet Widodo Herlambang Brawijaya
Corresponding Author:
Samudi Samudi | samudi.net@gmail.com

Copyright (C):
Samudi Samudi, Slamet Widodo, Herlambang Brawijaya

Abstract

A disease that is currently widespread today is caused by the spread of the coronavirus disease or what is commonly called COVID 19. This virus is very dangerous to health because it attacks organs in the human body from various sources, either from the air or direct touch. With the existence of COVID 19, it has an impact on all countries, especially the State of Indonesia, which consists of various islands, which are also affected by the COVID 19 virus. So that the central government takes a policy to carry out social distancing to every one to break the chain of spreading this virus, with this social distancing it has an impact on all activities that occur every day. As an impact on the learning process that usually takes place in class, it turns into online learning that uses several supporting applications in the learning process during the COVID 19 pandemic. With online learning from various applications, it attracts researchers to research with the K-Medoid Clustering Algorithm in using applications during the pandemic COVID 19.

Keyword: Data Mining, K-Medoid Algorithm, Clustering, Learning, Covid 19

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SAMUDI, Samudi; WIDODO, Slamet; BRAWIJAYA, Herlambang. The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic. Sinkron : Jurnal dan Penelitian Teknik Informatika, [S.l.], v. 5, n. 1, p. 116-121, oct. 2020. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10649>. Date accessed: 21 oct. 2020. doi: https://doi.org/10.33395/sinkron.v5i1.10649.
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References

Arianto, F. S. D., & P, N. (2020). PREDIKSI KASUS COVID-19 DI INDONESIA MENGGUNAKAN METODE BACKPROPAGATION DAN FUZZY TSUKAMOTO. (Jurnal Teknologi Informasi, 4(1), 120–127. Retrieved from http://jurnal.una.ac.id/index.php/jurti/article/view/1265%0A

Chesilia, S., Oktaviany, D., & Dewi, D. (2012). Sistem Informasi Manajemen Penjualan dan Persediaan Barang Berbasis Web pada CV. Matrik Jaya. Jurnal Sistem Informasi STMIK GI MDP, (x), 1–10. Retrieved from http://eprints.mdp.ac.id/1738/1/Jurnal-2012240058.pdf

Dwitri, N., Tampubolon, J. A., Prayoga, S., Zer, P. P. P. A. N. W. F. I. R. H., & Hartaman, D. (2020). PENERAPAN ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT PENYEBARAN PANDEMI COVID-19 DI INDONESIA. Jurnal Teknologi Informasi, 4(1), 128–132. Retrieved from http://jurnal.una.ac.id/index.php/jurti/article/download/1266/1104%0A

Etikasari, B., Puspitasari, T. D., Kurniasari, A. A., & Perdanasari, L. (2020). Sistem Informasi Deteksi Dini Covid-19. Jurnal Teknik Elektro Dan KOmputer, 9(2). Retrieved from https://ejournal.unsrat.ac.id/index.php/elekdankom/article/view/28278%0A

Haqien, D., & Rahman, A. A. (2020). PEMANFAATAN ZOOM MEETING UNTUK PROSES PEMBELAJARAN PADA MASA PANDEMI COVID-19. Susuan Artikel Pendidikan, 5(1). Retrieved from https://journal.lppmunindra.ac.id/index.php/SAP/article/view/6511%0A

Irwansyah, E., & Faisal, M. (2015). Advanced Clustering: Teori dan Aplikasi. Deepublish.

Juninda, T., Mustakim, & Andri, E. (2019). Penerapan Algoritma K-Medoids untuk Pengelompokan Penyakit di Pekanbaru Riau. Seminar Nasional Teknologi Informasi, Komunikasi Dan Industri (SNTIKI) 11, (November), 42–49. Retrieved from http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/8002/4465

Madhulatha TS. (2012). An Overview on Clustering Methods. IOSR Journal of Engineer, 2(4), 719–725.
Marlina, D., Putri, N. F., Fernando, A., & Ramadhan, A. (2018). Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak, 4(2), 64–71.

Mustakim. (2017). Effectiveness of K-means clustering to distribute training data and testing data on K-nearest neighbor classification. Journal of Theoretical and Applied Information Technology, 95(21), 5693–5700.

Pramesti, D. F., Lahan, Tanzil Furqon, M., & Dewi, C. (2017). Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 1(9), 723–732. https://doi.org/10.1109/EUMC.2008.4751704

Sabzi A, Farjami Y, Z. M. (2011). An Improved Fuzzy K-Medoids Clustering Algorithm with Optimized Number of Clusters. IEEE International Conference on Hybrid Intelligent System, 206–210.

Sindi, S., Ratnasari, W., Ningse, O., Sihombing, I. A., Zer, F. I. R. H., & Hartama, D. (2020). Analisis algoritma k-medoids clustering dalam pengelompokan penyebaran covid-19 di indonesia, 4(1), 166–173.

Tamura, Y., & Miyamoto, S. (2014). Two-Stage Clustering Using One-Pass K-Medoids and Medoid-Based Agglomerative Hierarchical Algorithms. In IEEE International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent System (pp. 484–488). Kitakyushu.
Tim Komunikasi Komite Penanganan Corona Virus Disease 2019 (Covid-19) dan Pemulihan Ekonomi Nasional. (2020). Kesembuhan Covid-19 di Indonesia Tembus 100.000. Retrieved from https://covid19.go.id/p/berita/kesembuhan-covid-19-di-indonesia-tembus-100000

Yulianto, E., Cahyani, P. D., & Silvianita, S. (2020). Perbandingan Kehadiran Sosial dalam Pembelajaran Daring Menggunakan Whatsapp group dan Webinar Zoom Berdasarkan Sudut Pandang Pembelajar Pada Masa Pandemic COVID-19. Jurnal Riset Teknologi Dan Inovasi Pendidikan, 3(2), 331–341. Retrieved from https://journal-litbang-rekarta.co.id/index.php/jartika/article/view/277%0A