<![CDATA[Background: The legislative proposal for the revision of the Indonesian Military Draft Bill (RUU TNI) has incited significant public controversy, largely articulated in digital arenas like YouTube. This revision fuels widespread apprehension about the potential revival of the military's dual-function role (Dwifungsi ABRI) and its potential detriment to civilian supremacy.Aims: This study aims to quantify and analyze public sentiment towards the RUU TNI, as expressed in Indonesian YouTube comments, using a Deep Learning approach combining FastText embedding and Long Short-Term Memory (LSTM) classification.Methods: A dataset of 520 Indonesian comments was collected and manually labeled into three classes: positive, neutral, and negative. The methodology included comprehensive text preprocessing, feature extraction via FastText word embeddings (300 dimensions), and sentiment classification using the LSTM architecture. The model was rigorously evaluated using a confusion matrix across standard metrics, including accuracy, precision, recall, and F1-score.Result: The dataset exhibited significant class imbalance, dominated by negative sentiment (42.31%). The optimal LSTM configuration, tested with Stratified K-Fold Cross Validation, yielded an accuracy of 42.39%, a precision of 43%, a recall of 47%, and an F1-score of 60%. Topic modeling via LDA revealed dominant themes criticizing the bill's quality, state corruption, and legislative integrity. The low accuracy, barely surpassing the baseline, suggests that the model struggled with the limited and imbalanced data.Conclusion: Public sentiment regarding the RUU TNI is strongly negative and critical, reflecting deep concerns about democracy and state institutions. While the FastText-LSTM pipeline was established, its classification performance was severely constrained by the small, highly imbalanced dataset and the complex nature of political discourse. Future research must utilize advanced Transformer models (e.g., IndoBERT) and larger, balanced datasets to achieve meaningful classification performance on this critical socio-political domain.]]>
