Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network
Abstract: Sentiment analysis
is a computational research of opinion sentiment and emotion which is expressed
in textual mode. Twitter becomes the most popular communication device among
internet users. Deep Learning is a new area of machine learning research. It aims
to move machine learning closer to its main goal, artificial intelligence. The
purpose of deep learning is to change the manual of engineering with learning.
At its growth, deep learning has algorithms arrangement that focus on
non-linear data representation. One of the machine learning methods is Deep
Belief Network (DBN). Deep Belief Network (DBN), which is included in Deep
Learning method, is a stack of several algorithms with some extraction features
that optimally utilize all resources. This study has two points. First, it aims
to classify positive, negative, and neutral sentiments towards the test data.
Second, it determines the classification model accuracy by using Deep Belief
Network method so it would be able to be applied into the tweet classification,
to highlight the sentiment class of training data tweet in Bahasa Indonesia.
Based on the experimental result, it can be concluded that the best method in
managing tweet data is the DBN method with an accuracy of 93.31%, compared with Naive Bayes method which has an accuracy of
79.10%, and SVM (Support Vector Machine) method with an accuracy of 92.18%.
Penulis: Ira zulfa, Edi Winarko
Kode Jurnal: jptinformatikadd170004
