CD Skripsi
Pengelompokan Status Pasien Covid-19 Menggunakan Metode K-Means Clustering (Studi Kasus: Rsud Arifin Achmad Provinsi Riau)
ABSTRACT
Corona viruses (CoV) are part of a family of viruses that cause illness ranging from the flu to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). The disease caused by the corona virus, also known as COVID-19. Many research have been done to build a clustering system especially on health department, in particular for COVID-19 study. This research aims to cluster COVID-19 patients so that patients get the right treatment, it can also be an illustration for the medical side of COVID-19 patients treated from April to June 2021 at the Arifin Achmad Hospital, Riau Province and measure the level of accuracy of the method used. This study uses the K-Means Clustering method in grouping data and uses the Silhouette Coefficient to see how good the quality of the resulting cluster is. The stages in this research are data preparation, data preprocessing, clustering process with K-Means, testing clustering results using Silhouette Coefficient and concluding the results Based on the results of the study, that grouping the status of COVID-19 patients using 20 variables and conduct experiments on 2nd to 5th cluster has a best Silhouette Coefficient value on 3rd cluster which is 0,166408759, where 1st cluster dominated by patients with moderate clinical classification degree, 2nd cluster dominated by patients with mild clinical classification degree, and 3rd cluster dominated by patients with critical clinical classification degree. Based on Silhouette Coefficient obtained so clustering COVID-19 patient data with 500 patient using K-Means Clustering method did not achieve satisfactory results because it is included in the no structure group.
Keywords: Data Mining, COVID-19, K-Means Clustering, Python, Silhouette Coefficient.
Tidak tersedia versi lain