<![CDATA[The large volume and informal nature of Indonesian social media text make manual sentiment analysis slow, inconsistent, and difficult to scale. This study evaluates a Long Short-Term Memory (LSTM) model for classifying public comments from X/Twitter into positive, neutral, and negative sentiment. A balanced dataset of 3,000 public Indonesian-language posts collected from January to March 2026 was manually labeled into three equal classes. Duplicate, irrelevant, advertising, and empty posts were removed; the remaining text underwent case folding, noise removal, tokenization, and padding. The data were stratified into 2,400 training and 600 testing instances. The model used a 10,000-word vocabulary, 100-token sequences, a 128-dimensional embedding, 128 LSTM units, dropout of 0.5, and a softmax output layer. On the held-out test set, the model obtained 87.00% accuracy, 86.80% precision, 86.50% recall, and 86.60% F1-score. Positive sentiment produced the strongest class-level performance, whereas neutral comments were more difficult because factual, ambiguous, and mixed expressions provide weaker affective cues. The findings show that LSTM provides a useful baseline for three-class Indonesian social media sentiment classification. However, generalization remains limited by the single-platform, topic-dependent dataset and the absence of repeated or cross-domain evaluation.]]>
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