Clustering Tingkat Kedisiplinan Warga Bekasi Dalam Menjalankan Protokol Kesehatan Di Masa Pandemi Covid-19 Dengan Algoritme K-Means
Abstract
Health protocols during the Covid-19 pandemic are very necessary because health protocols can speed up breaking the chain of spreading the Covid-19 virus. Violations that are often found in the Bekasi city environment are related to health protocols, namely maintaining distance, wearing masks and washing hands, or using hand sanitizer. There are still many who do not comply with the rules of the health protocol. The purpose of knowing the cluster level of discipline towards health protocols into five clusters spread by the number of respondents in various sub-districts in the city of Bekasi with the categories of discipline, somewhat disciplined, rarely disciplined, less disciplined, and undisciplined. Data mining is the process of extracting data to obtain new information. The technique used in this research is simple random sampling. This study using the CRIPS-DM methodology. This study calculates the k-means algorithm by obtaining a value of k = 2. The results of the test using the RapidMiner Studio 9.3 tools obtained two clusters or 2 categories of discipline levels against health protocols, namely cluster 0 with a percentage of 55.08% which is categorized as the most disciplined level, and cluster 1 with a percentage of 44.92% which is categorized as the least disciplined level. The results of clustering are evaluated by using the Silhouette Coefficient with the best cluster, k = 2 with a value of 0.926989, which is the best cluster.
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