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Sentiment information is one type of information that can be obtained from social media. Sentiments can be interpreted as opinions and views of society which contain feelings. To analyze the value of sentiment whether the sentiment is a sentiment that tends to be neutral, negative, or positive, sentiment analysis can be used. language has its characteristics and uniqueness, Bengkulu language is no exception, because of this, it is necessary to model sentiment analysis for various languages. Sentiment modeling for the Bengkulu language is not yet available, therefore a sentiment analysis model for the Bengkulu language is developed by applying Long Short-Term Memory (LSTM), and architectural experiments for Long Short-Term Memory (LSTM) are carried out to obtain an architectural sentiment analysis model that produces the best value. The data used in the study amounted to 24,000 Bengkulu-language comments received from social media Instagram, Twitter, and Youtube. Experimental research 1 produces the best accuracy value compared to the results of testing in other experiments, with an accuracy value of 0.87 a precision value of 0.80, a recall value of 0.82, and an F1 score of 0.81

Keywords: Information, sentiment, Long Short- Term Memory (LSTM), Bengkulu Language, architecture, social media.

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How to Cite
Nurhaeni, S., Faurina, R., Utama, F. P., & Anggriani, K. (2023). Sentiment Analysis Komentar Berbahasa Bengkulu Menggunakan Long Short-Term Memory (LSTM). Pseudocode, 10(2), 117–125.