Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels


  • Hubban Arfi Pratama Universitas Labuhanbatu, Indonesia
  • Gomal Juni Yanris Universitas Labuhanbatu, Indonesia
  • Mila Nirmala Sari Hasibuan Universitas Labuhanbatu, Indonesia




Classification, Confusion Matrix, K-Nearest Neighbor (kNN), Naïve Bayes, Satisfaction Level


An amusement park is a location or place that can provide a special attraction to the public. This is because in amusement parks there is lots of entertainment provided. But not all amusement parks are liked by visitors, usually because the location is still not good enough. Therefore the authors make a study of the level of visitor satisfaction. This research was made so that the writer can determine whether or not the number of visitors is satisfied at the amusement park. To conduct this research, the authors used 2 methods with a classification model in data mining. The methods used are the K-Nearest Neighbor (kNN) method and the Naïve Bayes method. Study this is done using 100 visitor data. The classification results obtained from both methods give the same results. The results obtained were 77 satisfied visitor data at amusement parks and 23 dissatisfied visitors at amusement parks. The result of the two methods used is that many visitors are satisfied with the amusement park. The accuracy results obtained are also very good. This means that these two methods are very suitable to be used as a method with a classification model. The conclusion is that the amusement park has beauty and a great location that can give attraction to visitors. With this research it can be a reference that the K-Nearest Neighbor (kNN) method and the Naïve Bayes method are very suitable for carrying out a data classification.

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Author Biographies

Gomal Juni Yanris, Universitas Labuhanbatu, Indonesia



Mila Nirmala Sari Hasibuan, Universitas Labuhanbatu, Indonesia




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How to Cite

Arfi Pratama, H., Yanris, G. J. ., & Hasibuan, M. N. S. . (2023). Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1832-1851.

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