• Sekar Setyaningtyas STMIK Tegal
  • Bangkit Indarmawan Nugroho Sekolah Tinggi Manajemen Informatika dan Komputer Tegal
  • Zaenul Arif Sekolah Tinggi Manajemen Informatika dan Komputer Tegal


data mining, clustering, K-Means, systematic literature review


Data Mining is a method for analyzing future patterns and characteristics as well as gathering unexpected,

never-before-seen information from large databases. In data mining, clustering is one of the useful

techniques for analyzing data. One of the data mining algorithms is the K-Means algorithm, which is a

clustering technique based on distance division. The goal to be achieved in this paper is to analyze the

clustering technique using the K-Means algorithm in data mining by conducting an in-depth review and

searching through the literature selected based on the criteria and studies that will be selected to answer

research questions. Systematic Literature Review (SLR) is a method that aims to identify and find research

results with techniques based on specific procedures from comparison results. Based on the literature on

the selection of journal publications, Pattern Recognition, Knowledge-Based Systems, Applied Soft

Computing and IEEE Access can be the main references related to the K-Means algorithm. The results of

the comparison show that Euclidean Distance has the advantage of better distance calculation, so that this

method can be used as the main choice related to the calculation theory of the K-Means algorithm.


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How to Cite

Sekar Setyaningtyas, B. . Indarmawan Nugroho, and Z. . Arif, “TINJAUAN PUSTAKA SISTEMATIS PADA DATA MINING: STUDI KASUS ALGORITMA K-MEANS CLUSTERING”, JTIF, vol. 10, no. 2, pp. 52–61, Oct. 2022.