Grouping Number of Library Members For Determining the Location of Socialization Using Clustering Method


  • Sella Dwi Pratiwi STMIK KAPUTAMA
  • Achmad Fauzi
  • I Gusti Prahmana



Clustering, Data Mining, K-Means, Outreach.


The high use of smartphones at this time led to a decline in public interest in reading books in the library directly. Especially students and students. This is certainly a problem for the Langkat Regency Archives and Libraries Office. Socialization is needed to increase efforts to read interest in the community. The right socialization location must have several criteria so that the socialization carried out is right on target. The existence of a database for each member of the library will facilitate the location selection process. Data mining techniques can classify the number of library members based on the results of large data analysis into information in the form of patterns. The clustering method is a method in data mining that can analyze data with the aim of grouping data based on the same characteristics. The K-Means algorithm is a simple algorithm for classifying a large number of objects with certain attributes into clusters which are usually used in data mining.


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How to Cite

Dwi Pratiwi, S., Fauzi, A., & Prahmana, I. G. (2023). Grouping Number of Library Members For Determining the Location of Socialization Using Clustering Method. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 3(1), 120–127.