Comparison Performance of Naive Bayes Classifier and Support Vector Machine Algorithm for Twitter’s Classification of Tokopedia Services

Abstract

Abstract Tokopedia is one of the online shopping centers in Indonesian that carries the business model marketplace. Positive and negative opinions in Twitter from Tokopedia users about company services are source of information for the management. Naive Bayes Classification (NBC) and Support Vectore Machine (SVM) are techniques in data mining used to classify data or users opinion. The algorithm of NBC is very simple since it only use text frequency to compute the posterior probability for each classes. While SVM algorithm is more complex than NBC. SVM develop hyperplane equation which separate data into classes perfectly. The researcher wants to compare the performance of the NBC and SVM algorithms and use them to classify user opinions on Tokopedia’s services, because these two algorithms have different approaches and difficulty levels. Classification included positive and negative class only. Accuracy, precision and recall value are used to compare the performance of both algorithms. Research evaluation shows that SVM linear kernel technique outperform NBC technique with the accuracy 83.34%.