<![CDATA[The increasing number of internet users is directly in line with the increasing number of data on the internet that is available for analysis, especially data in text form. The availability of this text data encourages a lot of sentiment analysis research. However, it turns out that the availability of abundant text data is also one of the challenges in sentiment analysis research. Datasets that consist of long and complex text documents require a different approach. In this study, LSTM was chosen to be used as a sentiment classification method. This research uses a movie review dataset that consists of 25,000 review documents, with an average length per review is 233 words. The research uses CBOW and Skip-Gram methods on word2vec to form a vector representation of each word (word vector) in the corpus data. Several dimensions of the word vector was used in this research, there are 50, 60, 100, 150, 200, and 500, this tuning parameter is used to determine their effect on the resulting accuracy. The best accuracy around 88.17% is obtained at the word vector 100 dimension and the lowest accuracy is 85.86% at the word vector 500 dimension.]]>
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