In this increasingly competitive world of technology, there needs to be efforts to improve the quality of educational institutions. However, it is often constrained by data processing that runs less than maximum and is less explored further. Therefore, Educational Data Mining as one of the clumps in data mining is tasked with uncovering student characteristics and behaviors hidden in a form of data that needs to be analyzed first. Based on these techniques, efforts to create a quality academic life by reducing the risk of student failure can be realized through a pedagogical plan of learning in the future. Through the following techniques, efforts to find association rules, group and classify data comparisons that have the best ability and value in the data mining process can be realized by implementing various tools such as WEKA, R, and Orange.
CITATION STYLE
Triayudi, A., Iksal, & Haerani, R. (2022). Data Mining K-Means Algorithm for Performance Analysis. In Journal of Physics: Conference Series (Vol. 2394). Institute of Physics. https://doi.org/10.1088/1742-6596/2394/1/012031
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