PERBANDINGAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) PADA ANALISIS SENTIMEN

UTARY, FENNY and Yusliani, Novi and Marieska, Mastura Diana (2023) PERBANDINGAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) PADA ANALISIS SENTIMEN. Undergraduate thesis, Sriwijaya University.

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Abstract

The study aims to perform a sentiment analysis in user review data to find out user satisfaction in the Spotify apps. Sentiment analysis is the process of document classification and in this study is divided into two parts, namely positive class and negative class. Classification is very important in searching documents for developers. The classification process begins by dividing the data collection into training data and testing data. Training data used Naïve Bayes method and Support Vector Machine method to obtain classification model for class determination on testing data. Both of the method is used to classify the results of user review data written on review section of Spotify apps so as to produced the desired classification automatically. The results of this study shows that Support Vector Machine gets better score with an accuracy of 83%, precision of 89%, recall of 64%, and f-measure of 74% than Naïve Bayes with an accuracy of 61%, precision of 57%, recall of 43%, and f-measure of 49%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, Comparative Study
Subjects:	P Language and Literature > P Philology. Linguistics > P98-98.5 Computational linguistics. Natural language processing
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Fenny Utary
Date Deposited:	03 Aug 2023 08:43
Last Modified:	03 Aug 2023 08:43
URI:	http://repository.unsri.ac.id/id/eprint/124262

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