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All Journal Indonesian Journal of Applied Technology and Innovation Science
Jane Astrid Ariani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Performance Comparison K-Nearest Neighbor, Naive Bayes, and Decision Tree Algorithms for Netflix Rating Classification Zulkarnain Zulkarnain; Risma Mutia; Jane Astrid Ariani; Zidny Alfian Barik; Habil Azmi
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1104

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.
Co-Authors Habil Azmi Risma Mutia Zidny Alfian Barik Zulkarnain Zulkarnain