Perbandingan Algoritma K-Means Dan K-Medoids Dalam Pengelompokkan Tingkat Kebahagiaan Provinsi Di Indonesia
Abstract
− Currently what is happening, indicators of the success of a region's development are still determined by the factors of economic growth and poverty, even though the measurement in terms of the economics achieved is not accurate, high economic growth does not always promise community satisfaction, because there may be gaps between communities. then what must be paid more attention is economic growth that can make people happy without any gaps. The happiness of the residents of an area will affect the success of development and social development in the community. Happiness can be used as a measure that can describe the welfare achieved by each individual. In the study, measurements and groupings of which provinces were included in the happiest and less happy provinces in Indonesia were carried out with 3 variables; index of satisfaction, feelings and meaning of life, using the comparison algorithm k-Means and k-Medoids then tested using the RapidMiner Studio application. Based on the DBI k-Medoids validity value of 0.648, it is smaller than the DBI k-Means validity value of 7.52. Produces 2 clusters, namely cluster 0 (the province cluster is less happy) and cluster 1 is the happiest province cluster
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