Open AccessImplementation of the Simple Additive Weighting Method in Determining Centroids in the Process of Clustering the Poor in Kakatpenjalin Village, Lamongan Regency
Author(s) -
Arifan Dwi Maulana,
Indra Gita Anugrah
Publication year - 2021
Publication title -
journal of development research
Language(s) - English
Resource type - Journals
eISSN - 2579-9347
pISSN - 2579-9290
DOI - 10.28926/jdr.v5i2.147
Subject(s) - cluster analysis , weighting , centroid , cluster (spacecraft) , k medians clustering , data mining , mathematics , selection (genetic algorithm) , point (geometry) , process (computing) , medoid , computer science , value (mathematics) , simple (philosophy) , statistics , fuzzy clustering , artificial intelligence , cure data clustering algorithm , medicine , philosophy , geometry , epistemology , radiology , programming language , operating system
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.