Open AccessSENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD
Author(s) -
Zetta Nillawati Reyka Putri,
Muhammad Muhajir
Publication year - 2021
Publication title -
jurnal riset informatika
Language(s) - English
Resource type - Journals
eISSN - 2656-1743
pISSN - 2656-1735
DOI - 10.34288/jri.v4i1.262
Subject(s) - support vector machine , sentiment analysis , oversampling , recall , computer science , preprocessor , class (philosophy) , artificial intelligence , value (mathematics) , the internet , precision and recall , machine learning , psychology , world wide web , cognitive psychology , computer network , bandwidth (computing)
At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.