IMPLEMENTASI METODE DATA MINING K-MEANS CLUSTERING TERHADAP DATA PEMBAYARAN TRANSAKSI MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON PADA CV DIGITAL DIMENSI

Dodi Alexsander Manalu(1), Goldie Gunadi(2*),

(1) STMIK Widuri Jakarta
(2) STMIK WIDURI
(*) Corresponding Author

Abstract


CV Digital Dimensi is a company engaged in the printing sector, which is a subsidiary of the XG Group located in Jakarta. In order to be able to compete with other companies, the company does not only focus on products and services but also efforts to build personal relationships with customers. Existing payment transaction data so far have not been utilized as much as possible in determining the company's business strategy, therefore the implementation of data mining is necessary to analyze and explore the available data to find new information that is more valuable and useful for the company. The K-Means Clustering method is a data mining technique to obtain data groups by maximizing the similarity of characteristics within the cluster and maximizing the differences between clusters. The purpose of this study is to apply the K-Means Clustering method to group sales transaction data on CV Digital Dimensi and display the results in the form of visual graphics using the Python programming language and Scikit-Learn library. The results of this study succeeded in classifying sales transaction data into five clusters and can be used as a reference in determining the company's business strategy.

 


Keywords


Data Mining, K-Means, Clustering, Cluster, Python, Scikit-Learn, Payment

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DOI: https://doi.org/10.37365/jti.v8i1.131

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