Klasterisasi Persebaran Virus Corona (Covid-19) Di DKI Jakarta Menggunakan Metode K-Means
DOI:
https://doi.org/10.21111/fij.v5i2.4905Keywords:
corona, jakarta, klasterisasi, k-meansAbstract
AbstrakCorona virus (COVID-19) merupakan jenis virus baru yang ditemukan pada manusia di propinsi Wuhan, Cina pada bulan Desember 2019. Virus ini dapat menular dari manusia ke manusia melalui tetesan kecil (droplet) dari hidung atau mulut pada saat batuk, bersin, atau berbicara. Oleh karena itu, di masa pandemi ini sangat penting untuk menjaga jarak dengan orang lain dan menghindari wilayah dengan persebaran COVID-19 yang tinggi. Pada penelitian ini dilakukan klasterisasi persebaran virus Corona di DKI Jakarta dengan menerapkan metode data mining. Pengelompokan dilakukan berdasarkan parameter jumlah ODP, PDP, kasus Positif, pasien sembuh dan pasien meninggal. Pada penelitian ini, untuk melakukan klasterisasi data digunakan metode K-Means dan metode pengukuran jarak Euclidean. Penelitian ini menghasilkan prototipe aplikasi pengelompokan data persebaran pasien Covid-19. Berdasarkan pengujian, jumlah klaster yang direkomendasikan adalah 9 klaster. Hasil penelitian ini diharapkan dapat membantu pemerintah DKI Jakarta dalam mengambil keputusan strategis dalam mengurangi persebaran virus Corona di DKI Jakarta.Kata kunci: corona, Jakarta, klasterisasi, k-means Abstract[Corona Virus (Covid-19) Clustering in Jakarta using K-Means Method] Coronavirus (COVID-19) is a new type of virus found in humans in the province of Wuhan, China in December 2019. This virus can be transmitted from person to person through small droplets from the nose or mouth when coughing, sneezing, or talking. Therefore, during this pandemic, it is very important to keep your distance from other people and avoid areas with a high spread of COVID-19 In this study, the distribution of the Coronavirus in DKI Jakarta was clustered by applying the data mining method. The clustering was carried out based on the parameters of the number of ODP, PDP, positive cases, patients recovered and patients died. In this study, to perform data clustering, the K-Means method, and the Euclidean distance measurement method were used. This study produced a prototype application for the distribution of Covid-19 patient distribution data. Based on the test, the recommended number of clusters is 9 clusters. The results of this study are expected to help the DKI Jakarta government in making strategic decisions in reducing the spread of the Coronavirus in DKI Jakarta.Keywords: corona, Jakarta, clustering, k-meansReferences
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