<![CDATA[Using natural language processing methods, this study investigates public perceptions of the development of the Indonesian Capital City (IKN) on social media X. Previous studies predominantly relied on isolated sentiment classification or standalone topic modeling, which failed to explain the specific underlying context behind public sentiment. To address this gap, this study proposes a novel two-stage framework integrating Fine-Tuned IndoBERT (optimized via Low-Rank Adaptation/LoRA) for sentiment classification and BERTopic for topic extraction. A total of 12,000 raw comments were harvested via web crawling, preprocessed, validated through automated exact-match deduplication and noise filtering, and labeled into positive, negative, and neutral classes. Performance evaluation using Confusion Matrix and classification metrics revealed that Fine-Tuned IndoBERT achieved optimal performance with an accuracy of 91.83%, a macro precision of 91.84%, a macro recall of 91.54%, and a macro F1-score of 91.93%. The integration with BERTopic configured using Sentence Transformer, UMAP, HDBSCAN, and class-based TF-IDF (c-TF-IDF) components successfully extracted nine main topic clusters and mapped them against sentiment categories. The results confirm that positive sentiments dominate physical development, logistics, and digital transformation topics, whereas negative sentiments concentrate heavily on environmental impacts, budget transparency, and indigenous rights. This study provides an actionable, integrative decision-support model for public policy analysis.]]>
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