<![CDATA[Customer reviews are opinions on the quality of goods or services that consumers perceive. Customer reviews contain useful information for both consumers and providers of goods or services. The availability of a large number of customer reviews on the websiterequires a framework for extracting sentiment automatically. A customer review often contains many aspects, so the Aspect Based Sentiment Analysis (ABSA) should be used to determine the polarity of each aspect. One of the important tasks in ABSA is Aspect Category Detection. The application of Machine Learning Methods for Aspect Category Detection has been mostly done in the English language domain, but in the Indonesian language domain,there are still a few. This study compares the performance of three machine learning algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM),and Random Forest (RF),on Indonesian language customer reviews using Term Frequency-Inverse Document Frequency (TF-IDF) as term weighting. The results showthat RFperformsthe best,compared to NB and SVM,in three different domains, namely restaurants, hotels,and e-commerce,with the f1-scoresfor each domainare84.3%, 85.7%, and 89.3%.]]>
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