Comparison of the K-Means Algorithm and C4.5 Against Sales Data

Authors

  • Eko Bambang Wijaya Universitas Prima Indonesia
  • Abdi Dharma Universitas Prima Indonesia
  • Daniel Heyneker Universitas Prima Indonesia
  • Jeff Vanness Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i2.12224

Abstract

In general, the process of collecting and grouping data requires a
long process. And if it has to be grouped manually it takes a very long time.
Therefore, data mining is a solution for clustering data - a lot of data to
classify it. In this research conducted at CV.Togu - Togu On Medan Branch,
data mining is applied using the K-Means process model and the C4.5
algorithm which provides a standard process for using data mining in various
fields used in classification because the results of this method easy to
understand and easy to interpret. . The K-means method is a non-herarical
method which is an algorithmic technique for grouping items into k clusters
by minimizing the distance of the SS (sum of square) to the cluster centroid.
In the K-means method, the number of clusters can be determined by the
researcher himself. And the testing methods used to measure cluster quality
are the Silhouette Coefficient and the Elbow Method. Based on the research
conducted, there are significant differences before and after using the two
methods. The results of the K-Means algorithm will be compared with the
results of the C4.5 algorithm in the form of rules (decision trees). This
research produces data on goods that have the highest level of
sales/behavior

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How to Cite

Wijaya, E. B., Dharma, A., Heyneker, D., & Vanness, J. . (2023). Comparison of the K-Means Algorithm and C4.5 Against Sales Data. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 741-751. https://doi.org/10.33395/sinkron.v8i2.12224

Issue

Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023

Section

Articles

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Copyright (c) 2023 Eko Bambang Wijaya, Abdi Dharma, Daniel Heyneker

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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