Intisari— Pandemi COVID-19 merupakan suatu kejadian yang menimbulkan banyak sekali data yang sulit diolah. Data-data yang sangat penting seperti jumlah infeksi yang terkonfirmasi, jumlah kematian, dan jumlah orang yang pulih dapat diperoleh dari database seperti Kaggle, akan tetapi data tersebut perlu diolah lagi agar dapat menjadi berguna. Tujuan dari penelitian ini adalah untuk memperoleh dan mengolah data COVID-19 yang terdapat pada Kaggle mengunakan metode Data Mining yaitu K-Means Clustering Untuk K-Means Clustering pada penelitian ini, akan digunakan tiga metode untuk mengolah data yaitu pengolahan menggunakan software Microsoft Excel, dan software Data Mining yaitu Weka dan KNIME. Dari hasil pengolahan data, diperoleh dua cluster data, dimana cluster 2 memiliki jumlah terjangkit dan meninggal yang lebih tinggi dibandingkan dengan cluster 1, maka daerah-daerah cluster tersebut perlu diprioritaskan penanganannya.Abstract— The COVID-19 pandemic is an event that has generated lots of data that are difficult to process. Crucial data such as number of confirmed infections, number of deaths, and number of people recovered can be obtained from databases such as Kaggle, however these data needs to be processed further to become useful. The purpose of this research is to obtain and process COVID-19 data contained in Kaggle using Data Mining method namely K-Means Clustering Therefore, to process Big Data such as this, a Data Mining technique can be used which is Clustering. For K-Means Clustering in this research, there will be three methods used to process this data which is processing using the Microsoft Excel software, and using the Weka and KNIME Data Mining software. From the data processing results, two data clusters are obtained, in which cluster 2 have higher number of confirmed cases and deaths compared to cluster 1, thus the regions in that cluster needs priority in handling.
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