Komparasi Metode Naive Bayes dan K-Nearest Neighbors Terhadap Analisis Sentimen Pengguna Aplikasi Zenius

Authors

  • Tegar Abdillah Universitas Jambi
  • Ulfa Khaira Universitas Jambi
  • Benedika Ferdian Hutabarat Universitas Jambi

DOI:

https://doi.org/10.33998/processor.2024.19.1.1596

Keywords:

Naïve Bayes, K-Nearest Neighbors, Sentiment Analysis, Zenius, Performance Evaluation

Abstract

The purpose of this research is to compare the performance of Naive Bayes and K-Nearest Neighbor (KNN) methods in analyzing user sentiment on the Zenius application. The evaluation is done by checking the precision, precision, recall, and F1-Score scores of both methods as well as visualizing the results of sentiment analysis with one of the methods used. The advantage of this research is a deeper understanding of how Naive Bayes and KNN techniques work in sentiment analysis in the context of the Zenius app. Furthermore, this research aims to evaluate the performance results of two techniques, Naive Bayes and KNN, in sentiment analysis. From the results of testing split data scenarios using Split Validation with training data and testing data 90:10. Naive Bayes accuracy reached 88.41%, while KNN reached 100%. In this study, KNN outperformed Naive Bayes in terms of precision, recall, and F1-Score values. The results of data visualization show that the direction of the sentiment generated tends to be positive. This study not only provides a deeper understanding of the performance of Naive Bayes and KNN techniques in sentiment analysis for the Zenius application, but also provides a comprehensive evaluation of their performance. This research is expected to serve as a guide for developing more effective sentiment analysis methods for similar applications in the future.

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Published

2024-05-03

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DOI:

10.33998/processor.2024.19.1.1596

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How to Cite

Abdillah, T., Khaira, U., & Hutabarat, B. F. (2024). Komparasi Metode Naive Bayes dan K-Nearest Neighbors Terhadap Analisis Sentimen Pengguna Aplikasi Zenius. Jurnal PROCESSOR, 19(1). https://doi.org/10.33998/processor.2024.19.1.1596