Implementation of the Simple Additive Weighting Method in Determining Centroids in the Process of Clustering the Poor in Kakatpenjalin Village, Lamongan Regency

Clustering is an algorithm in a decision support system that functions to organize an object into groups of data. In the clustering process, of course, a cluster centre is needed by the desired data group. However, the clustering process has a problem. Related research states that the results of k-means clustering can influence the selection of cluster centre points. Random selection of cluster centre points can result in different clustering results in the same data group. Not only on k-means, but k-medoids also have the same problem. So that to produce a good cluster, you must start by choosing the right cluster centre. To solve this problem, the Simple Additive Weighting method is used to select the centre point of the cluster. Simple Additive Weighting selects the centre point of the cluster by adding and summarizing the dataset. The summation is done by giving weight to each criterion and each criterion has its alternative value. From this weighted addition, the final value will be obtained. From the sum of SAW, then one of the objects with the highest and lowest values ​​can be taken to serve as the centre of the cluster

Implementation of the Simple Additive Weighting Method in Determining Centroids in the Process of Clustering the Poor in Kakatpenjalin Village, Lamongan Regency

Clustering is an algorithm in a decision support system that functions to organize an object into groups of data. In the clustering process, of course, a cluster centre is needed by the desired data group. However, the clustering process has a problem. Related research states that the results of K-Means clustering can influence the selection of cluster centre points (centroids). Random selection of cluster centre points can result in different clustering results in the same data group. Not only on K-Means, but K-Medoids also have the same problem. So that to produce a good cluster, you must start by choosing the right centroids. To solve this problem, the Simple Additive Weighting method is used to select the centre point of the cluster. Simple Additive Weighting selects the centre point of the cluster by adding and summarizing the dataset. The summation is done by giving weight to each cri-terion and each criterion has its alternative value. From this weighted addition, the final value will be obtained. From the sum of SAW, then one of the objects with the highest and lowest values can be taken to serve as the centre of the cluster. From the results of the research conducted, it was found that the determination of the centroid using the SAW ranking can produce better clusters than con-ventional clustering. From five times of testing, it was found that the cluster results were consistent or there was no change in the cluster members. The location of the poor and rich clusters is easy to iden-tify according to the centroid used, this can happen because by ranking the dataset, it can be seen which data is used for the poor cluster centroid and the capable cluster centroid