  Twitter is one of the top influenced social media which has a million number
of active users. It is commonly used for microblogging that allows users to
share messages, ideas, thoughts and many more. Thus, millions interaction such
as short messages or tweets are flowing around among the twitter users
discussing various topics that has been happening world-wide. This research
aims to analyse a sentiment of the users towards a particular trending topic
that has been actively and massively discussed at that time. We chose a hashtag
\textit{\#kpujangancurang} that was the trending topic during the Indonesia
presidential election in 2019. We use the hashtag to obtain a set of data from
Twitter to analyse and investigate further the positive or the negative
sentiment of the users from their tweets. This research utilizes rapid miner
tool to generate the twitter data and comparing Naive Bayes, K-Nearest
Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to
classify the sentiment of the twitter data. There are overall 200 labeled data
in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron
classification outperformed the other two methods on 11 experiments with
different size of training-testing data split. The two classifiers are
potential to be used in creating sentiment analyzer for low-resource languages
with small corpus.
