GROUPING OF SEWING TOOL ASSISTANCE RECIPIENTS USING K-MEANS CLUSTERING ANALYSIS - Nana Suarna; Herman; Nining Rahaningsih; Rini Astuti; Yudhistira Arie Wijaya (Bajang Institute, 2022)
Aid programs for underprivileged communities need continuous data collection in overcoming welfare problems, what has happened so far is by providing direct assistance to very poor families in every village in Indonesia. Related to this, the problem that has occurred so far is that the direct assistance program is not right on target, because many deserving families should not receive the assistance. The data obtained from the village government is not accurate, it is found that data is considered invalid. This study aimed to determine the distribution of sewing equipment recipients in the best cluster, for sewing equipment recipients in Greged Village, Cirebon Regency. As one way to improve data accuracy, a computational method or model is needed in the form of a data mining algorithm using the k-means clustering method to generate priority groups among hundreds of citizens or the poor. The stages start from data collection, training data, and testing data that consider several criteria from household information, economic conditions, housing conditions, and the number of household members in Greged Village, Cirebon Regency. The results of the tests carried out using 155 data with the best level of accuracy were in the K3 cluster with the Davies Bouldin Index’s value o: - 0.584. With the K-Means method, it is very appropriate to determine the recipient of the sewing equipment program in Greged Village.