Implementasi Graph Clustering Algorithm Modification Maximum Standard Deviation Reduction (MMSDR) dalam Clustering Provinsi di Indonesia Menurut Indikator Kesejahteraan Rakyat
(1) Universitas Indraprasta PGRI
(2) Universitas Indraprasta PGRI
(3) Universitas Indraprasta PGRI
(*) Corresponding Author
Abstract
The population of Indonesia from year to year has increased. The increase in population must also be accompanied by increased economic growth in Indonesia. The increase in economic growth in Indonesia is marked by the reduction in the number of poor people in Indonesia. In addition, the increase in economic growth is reflected in the equitable distribution of public income in the country. Even though there are still many Indonesian people who are not yet prosperous in economic terms. To overcome, it is necessary to have clustering and characteristics of 34 provinces in Indonesia by implementing the Modification Maximum Standard Deviation Reduction (MMSDR) graph clustering algorithm. The data used are indicators of public welfare in 2017 obtained from the Central Statistics Agency. There are 9 indicators of community welfare used in this research. There are four stages in the MMSDR algorithm namely the "MST", "Subdivide", "Biggest Stepping" and "Create Clusters" processes. The results of this study can be seen from the distance between the nodes or between one province and another province produced 22 clusters. From the cluster results obtained using the MMSDR algorithm on welfare data, there are many clusters formed with cluster members formed at most two nodes (province).
Keywords: MMSDR, Clustering, Welfare of People
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DOI: http://dx.doi.org/10.30998/faktorexacta.v13i2.5863
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
