Performance Comparison K-Nearest Neighbor, Naive Bayes, and Decision Tree Algorithms for Netflix Rating Classification

Authors

  • Zulkarnain Zulkarnain Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Risma Mutia Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Jane Astrid Ariani Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Zidny Alfian Barik Al-Azhar University
  • Habil Azmi Al-Azhar University

Keywords:

Classification, Decision Tree, K-Nearest Neighbor, Naïve Bayes, Netflix

Abstract

Netflix is a streaming service platform that is growing along with the increasing number of internet users. This research aims to classify movie and TV show rating datasets on Netflix by comparing the KNN, Naive Bayes and Decision Tree algorithms to determine the accuracy comparison of the three algorithms. From the results of the analysis, it is found that the three algorithms produce a comparison of the accuracy of movie and tv show rating classification data on Netflix with different values. Based on the confusion matrix, namely Accuracy, Precision, and Recall, it is found that the Naive Bayes algorithm has the highest accuracy of 72%, the Decision Tree algorithm is 70% and the KNN algorithm has the lowest accuracy of 61%. From these results it can be stated that the Naive Bayes algorithm can classify movie and tv show rating data on Netflix better than compared to the other two algorithms.

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Published

2024-01-10