Sentiment Analysis of Student Review in Learning Management System Based on Sastrawi Stemmer and SVM-PSO

Abstract

In the learning management system, there are reviews from students of the learning process that has been done in a period. In this case, we use the review dataset to conduct sentiment analysis. The challenge of this dataset is the number of words that contain abbreviations and are not standard. So it challenges us to test the level of accuracy in the sentiment analysis process using several classification methods and sastrawi stemmer. Sastrawi stemmer is used to reduce features without changing the meaning data, Basic function of sastrawi is change words in the basic and eliminate nonessential or non-standard words with filtering concept. In the classification process, we use the SVM-PSO algorithm and compare it with other popular classification methods such as SVM, Naive Bayes and KNN. SVM-PSO is a combination of algorithms that is good to handle data with large dimensions and binary classification types. This is our reason for using SVM-PSO as the main classifer. Experimental results show that the use of sastrawi stemmer can reduce features by 32.58%. The accuracy of the classification process using SVM-PSO of 82.27% (with sastrawi stemmer) and 82.09% (without sastrawi stemmer), these results indicate that sastrawi stemmer influences the results of classification. SVM-PSO classification method has the highest level of accuracy compared to other classification methods, namely Naive Bayes gets an accuracy of 69.73%, K-NN gets an accuracy of 77.67% and SVM gets an accuracy of 81.52%. Based on the experimental results, SVM-PSO method has the best accuracy than any other method, and Sastrawi stemmer influences the level of accuracy.