Application of Data Mining using the K-Means Method for Visitor Grouping


  • Rahmayuni Syah Universitas Labuhanbatu, Indonesia
  • Marnis Nasution Universitas Labuhanbatu, Indonesia
  • Irmayanti Universitas Labuhanbatu, Indonesia




Keywords: Clustering; Confusion Matrix; Data Mining; K-Means; Scatter Plot


Grouping amusement ride visitor data is an important process that aims to identify certain patterns of visitors, enabling management to adjust marketing strategies and improve their services more effectively. This process begins with a data selection stage where relevant visitor data is collected and prepared for analysis. The next stage is data pre-processing, which involves cleaning the data from noise or irrelevant data, as well as ensuring the data is in a format suitable for analysis. After that, the data mining model design is carried out by selecting the most appropriate method for grouping visitor data. The next stage is testing and evaluating the model to verify its accuracy and effectiveness. The results of model testing show that visitor data can be categorized into three groups: C1 with 50 data, C2 with 20 data, and C3 with 48 data. The results of the model evaluation confirm that the designed model succeeded in classifying data with perfect accuracy, namely 100%. This success shows that the model is highly effective in identifying and segmenting visitor patterns, providing valuable insights for strategic decision making in service improvement and marketing. This success also opens up opportunities for the application of similar methods to other datasets in an effort to improve visitor experience and operational efficiency.

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How to Cite

Syah, R. ., Nasution, M. ., & Irmayanti, I. (2024). Application of Data Mining using the K-Means Method for Visitor Grouping. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1135 - 1147.

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