Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store


Lutfi Budi Ilmawan(1*); Muhammad Aliyazid Mude(2);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.

Keywords


Classification; Sentiment Analysis; Support Vector Machine;Naïve Bayes;Cross-validation

  
  

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Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v12i2.597.154-161 		  

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References


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