Open AccessSENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING
Author(s) -
Irwansyah Saputra,
Jose Andrean Halomoan,
Adam Bagusmugi Raharjo,
Cyra Rezky Ananda Syavira
Publication year - 2020
Publication title -
techno nusa mandiri/techno nusa mandiri
Language(s) - English
Resource type - Journals
eISSN - 2527-676X
pISSN - 1978-2136
DOI - 10.33480/techno.v17i2.1635
Subject(s) - decision tree , naive bayes classifier , sentiment analysis , computer science , precision and recall , artificial intelligence , classifier (uml) , decision tree learning , recall , transformation (genetics) , data mining , machine learning , natural language processing , pattern recognition (psychology) , support vector machine , biochemistry , chemistry , gene , linguistics , philosophy
A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%.