@thesis{thesis,
		author={Sintiya  .},
		title ={Kombinasi Single Linkage dengan K-Means Clustering untuk Pengelompokan Wilayah Desa Kabupaten Pemalang},
		year={2021},
		url={https://repository.ittelkom-pwt.ac.id/6458/},
		abstract={K-Means very dependent on determining the center cluster initial which will have an impact on the quality of clusters the resulting. This is found in the conventional method K-Means . Poverty is mostly experienced by rural communities and the low level of infrastructure services in rural areas is the background for policies and programs for rural infrastructure development. In implementing development programs, identification is needed based on the characteristics of the level of community welfare in each region so that policy making and development strategies are right on target. Therefore, we need an effort to group villages so that policy making is right on target. One Algorithm clustering that is widely used is the K-Means algorithm because it is quite simple, easy to implement, and has the ability to group large data groups very quickly. However, the K-Means algorithm has a weakness in determining the center cluster initial given. Initialization of centers cluster randomly may result formation cluster in changing (inconsistent). For this reason, the K-Means method needs to be combined with the hierarchical method in determining the center cluster initial. This combination method is referred to as Hierarchical K-Means which is a combination of methods hierarchical and partitioning, where the process is hierarchical used to find the initial center initialization cluster and the process partitioning to get cluster the optimal. The hierarchical method that will be tried in this research is the method single linkage. The combination of the single linkage and k-means algorithms with k = 4 in this study resulted in a silhouette coefficient value of 0.685 which is a viable or appropriate cluster category, while the evaluation measurement using the Davies Boulldin Index yields a value of 0.577 Kata Kunci : Cluster, Davies Bouldin Index, K-Means, Silhoutte Coefficient,, Single Linkage}
		}