Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia
-
Novianti Puspitasari
*
Universitas Mulawarman, Samarinda,
Indonesia
- Gidion Lempas Universitas Mulawarman, Samarinda, Indonesia
- Hamdani Hamdani Universitas Mulawarman, Samarinda, Indonesia
- Haviluddin Haviuddin Universitas Mulawarman, Samarinda, Indonesia
- Anindita Septiarini Universitas Mulawarman, Samarinda, Indonesia (*) Corresponding Author
Abstract
Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.
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References
A. N. Sihananto, A. P. Sari, H. Khariono, R. A. Fernanda, and D. C. M. Wijaya, “Implementasi Metode K-Means untuk Pengelompokan Kasus Covid-19 Tingkat Provinsi di Indonesia,” J. Inform. dan Sist. Inf., vol. 3, no. 1, pp. 76–85, 2022, doi: 10.33005/jifosi.v3i1.472.
G. D. Rembulan, T. Wijaya, D. Palullungan, K. N. Alfina, and M. Qurthuby, “Kebijakan Pemerintah Mengenai Coronavirus Disease (COVID-19) di Setiap Provinsi di Indonesia Berdasarkan Analisis Klaster,” JIEMS (Journal Ind. Eng. Manag. Syst., vol. 13, no. 2, 2020, doi: 10.30813/jiems.v13i2.2280.
M. W. Goni, D. Gustian, and F. Sembiring, “Implementasi K-Means Dalam Pengelompokan Penyebaran COVID-19 di Jawa Barat,” Progresif J. Ilm. Komput., vol. 17, no. 2, pp. 107–118, 2021.
Y. F. S. Y. Damanik, S. Sumarno, I. Gunawan, D. Hartama, and I. O. Kirana, “Penerapan Data Mining Untuk Pengelompokan Penyebaran Covid-19 Di Sumatera Utara Menggunakan Algoritma K-Means,” J. Ilmu Komput. dan Inform., vol. 1, no. 2, 2021.
A. Solichin and K. Khairunnisa, “Klasterisasi persebaran virus Corona (Covid-19) di DKI Jakarta menggunakan metode K-Means,” Fountain Informatics J., vol. 5, no. 2, pp. 52–59, 2020.
Z. Nabila, A. R. Isnain, P. Permata, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. Dan Sist. Inf., vol. 2, no. 2, pp. 100–108, 2021.
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, 2021.
D. Sepri and Y. Fimazid, “Pengelompokan Penyebaran Covid-19 di Kota Padang Menggunakan Algoritma K-Medoids,” Inf. Syst. Res. J., vol. 1, no. 2, pp. 39–45, 2021.
S. Samudi, S. Widodo, and H. Brawijaya, “Algoritma K-Medoids untuk Menentukan Clustering Data Covid-19 di DKI Jakarta,” JURSIMA (Jurnal Sist. Inf. dan Manajemen), vol. 10, no. 1, pp. 122–127, 2022.
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.
U. R. Gurning and Mustakim, “Penerapan Algoritma K-Means dan K-Medoid untuk Pengelompokkan Data Pasien Covid-19,” Build. Informatics, Technol. Sci., vol. 3, no. 1, p. 48−55, 2021, doi: 10.47065/bits.v3i1.1003.
A. Supriyadi, A. Triayudi, and I. D. Sholihati, “Perbandingan Algoritma K-Means dengan K-Medoids pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas,” J. Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 2, pp. 229–240, 2021, doi: 10.29100/jipi.v6i2.2008.
N. T. Luchia and Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids pada Pengelompokan Humidity , Temperature , dan Voltage di Data Center Perawang,” J. Inf. Syst. Res., vol. 4, no. 1, pp. 184–190, 2022, doi: 10.47065/josh.v4i1.2385.
F. Farahdinna, I. Nurdiansyah, A. Suryani, and A. Wibowo, “Perbandingan Algoritma K-Means dan K-Medoids dalam Klasterisasi Produk Asuransi Perusahaan Nasional,” J. Ilm. FIFO, vol. 11, no. 2, pp. 208–214, 2019, doi: 10.22441/fifo.2019.v11i2.010.
Athifaturrofifah, R. Goejantoro, and D. Yuniarti, “Perbandingan Pengelompokan K-Means dan K-Medoids Pada Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Studi Kasus : Data Titik Panas Di Indonesia Pada 28 April 2018),” J. EKSPONENSIAL, vol. 10, no. 2, pp. 143–152, 2019.
M. Herviany, S. P. Delima, T. Nurhidayah, and Kasini, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokkan Daerah Rawan Tanah Longsor di Provinsi Jawa Barat,” MALCOM (ndonesian J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 34–40, 2021.
D. Marlina, N. F. Putri, 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.
M. Wahyudi, S. Solikhun, and L. Pujiastuti, “Komparasi K-Means Clustering dan K-Medoids Clustering dalam Mengelompokkan Produksi Susu Segar di Indonesia Berdasarkan Nilai DBI,” J. Bumigora Inf. Technol., vol. 4, no. 2, pp. 243–254, 2022.
E. Luthfi and A. W. Wijayanto, “Analisis Perbandingan Metode Hirearchical, K-Means, dan K-Medoids Clustering dalam Pengelompokkan Indeks Pembangunan Manusia Indonesia,” INOVASI, vol. 17, no. 4, pp. 761–773, 2021.
K. Anam, B. Nurhakim, and C. Juliane, “Komparasi Algoritma Klasifikasi Data Mining Menggunakan Optimize Selection untuk Peminatan Program Studi,” Build. Informatics, Technol. Scince, vol. 4, no. 2, pp. 606–613, 2022, doi: 10.47065/bits.v4i2.2160.
Haviluddin et al., “A Performance Comparison of Euclidean, Manhattan and Minkowski Distances in K-Means Clustering,” in 2020 6th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Disaster Management, ICSITech 2020, 2020, pp. 184–188, doi: 10.1109/ICSITech49800.2020.9392053.
I. S. Damanik, S. R. Andani, and D. Sehendro, “Teknik Data Mining dalam Clustering Produksi Susu Segar di Indonesia dengan Algoritma K-Means,” BRAHMANA (Jurnal Penerapan Kecerdasan Buatan), vol. 1, no. 1, pp. 31–39, 2019, doi: 10.30645/brahmana.v1i1.5.
A. Septiarini, I. A. Thaher, and N. Puspitasari, “Pengelompokan Kualitas Kinerja Pegawai Menggunakan Metode K-Means Clustering,” Komputika J. Sist. Komput., vol. 11, no. 2, pp. 131–141, 2022, doi: 10.34010/komputika.v11i2.5518.
E. Irfiani and S. S. Rani, “Algoritma K-Means Clustering untuk Menentukan Nilai Gizi Balita,” JUSTIN (Jurnal Sist. dan Teknol. Informasi), vol. 6, no. 4, pp. 165–172, 2018.
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.
R. R. Muhima et al., Kupas Tuntas Algoritma Clustering: Konsep, Perhitungan Manual, dan Program. Yogyakarta: Andi, 2022.
L. P. Refialy, H. Maitimu, and M. S. Pesulima, “Perbaikan Kinerja Clustering K-Means pada Data Ekonomi Nelayan dengan Perhitungan Sum of Square Error (SSE) dan Optimasi nilai K cluster,” Techno.Com, vol. 20, no. 2, pp. 321–329, 2021, doi: 10.33633/tc.v20i2.4572.
B. Wira, A. E. Budianto, and A. S. Wiguna, “Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang,” RAINSTEK J. Terap. Sains Teknol., vol. 1, no. 3, pp. 53–68, 2019, doi: 10.21067/jtst.v1i3.3046.
I. Kamila, U. Khairunnisa, and 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, pp. 119–125, 2019.
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Copyright (c) 2023 Novianti Puspitasari, Gidion Lempas, Hamdani Hamdani, Haviluddin Haviuddin, Anindita Septiarini
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