GROUPING COMMUNITY BUSINESS DATA BASED ON THE TYPE OF BUSINESS USING THE CLUSTERING METHOD AT THE TANJUNG MERAHE VILLAGE OFFICE

Authors

  • Dian Lestari STMIK Kaputama, Binjai, Sumatera Utara

Keywords:

K-Means_Algorithm, Clustering, Data_Mining, Community Business Data, Matlab

Abstract

Grouping is an alternative that can be used to explore information so that it becomes new knowledge for everyone who wants to get it. and in this case the information to be explored is about community businesses in Tanjung Merahe Village, Finish District. This excavation was made to obtain information about the types of businesses that exist in Tanjung Merahe Village based on the age of the business in order to make it easier for the government to find out the type of business and its business income which can be further developed so that it becomes a superior product in Tanjung Merahe Village and can facilitate the Village Government in making decisions if there is assistance from the central government based on the type of business owned by the community. Writing this proposal uses the clustering method which is one of the data mining techniques for grouping community business data using the k-means algorithm clustering method. By applying 20 alternative data samples of student data and providing a total of 3 clusters, and utilizing 3 main criteria as research in this proposal, the resulting number of cluster 1 is 6 data, cluster 2 is 8 data. And cluster 3 as much as 6 data. This community business data grouping system is designed with the MATLAB application programming language and utilizes the GUI as its interface.

References

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Additional Files

Published

2023-11-23

How to Cite

Lestari, D. (2023). GROUPING COMMUNITY BUSINESS DATA BASED ON THE TYPE OF BUSINESS USING THE CLUSTERING METHOD AT THE TANJUNG MERAHE VILLAGE OFFICE . Journal of Mathematics and Technology (MATECH), 2(2), 84–92. Retrieved from http://journal.binainternusa.org/index.php/matech/article/view/149

Issue

Vol. 2 No. 2 (2023): Journal MATECH (November 2023)

Section

Articles

License

Copyright (c) 2023 Dian Lestari

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