Application of K-Means Clustering Method for City Grouping on Food Plant Productivity in North Sumatera - Junita Fadilah; Ismail Husein (UIN Sumatera Utara, 2019)
The development of population increases every year, causing food needs to expand to meet food needs by increasing food crop productivity so that food availability can be sufficient. Food crops consist of rice, corn, green beans, peanuts, cassava, and sweet potatoes. Productivity in each region has different characteristics, and therefore it is necessary to group the areas so that solution can be implemented by each of the components of the region. The purpose of this study is to group districts/cities in North Sumatera Province based on food crop productivity using the k-means clustering method. Clustering k-means is a method of grouping non-hierarchical data that attempts to partition existing data into one or more clusters or groups so that data that has the same characteristics are grouped into one same characteristic are grouped into other groups. The result of this study is the formation of 3 city district clusters, namely, cluster 1 amounting to 1 regency/city, cluster 2 totaling seven districts/cities, and cluster 3 totaling 25 districts/cities.