Clustering Pelaksanaan Vaksinasi di Jawa Tengah Menggunakan Metode K-Means

Abstract

Vaccines are a form of government responsibility in guaranteeing citizens' rights to health. In its implementation, the government seeks to fulfill the availability of vaccines for at least 208,265,720 residents. This number is the minimum number to be able to achieve herd immunity. To make it easier to show the vaccine achievements of each region, clustering can be done. The K-Means method is a non-hierarchical clustering method that is performed by partitioning data into predefined clusters. The research objects are 35 cities/districts in Central Java. The results of the data processing show that there are 2 optimal clusters, with information that cluster 1 is an area with a high vaccination achievement, while cluster 2 is an area with a low vaccination achievement.

Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma K-Means

“…Cluster yang terbentuk menghasilkan nilai Silhoutte Coefficient sebesar 0,78 atau strong structure. [10]. K-Means merupakan metode yang dapat mengkategorikan data dalam jumlah besar dengan komputasi yang relatif cepat dan irit waktu.…”