Social Media Sentiment Analyst on Danantara Using Support Vector Machine with TF-IDF Weighting
DOI:
https://doi.org/10.57152/ijatis.v3i2.3091Keywords:
Danantara, Sentiment Analysis, Social Media, Support Vector Machine, TF-IDFAbstract
The establishment of Badan Pengelola Investasi Daya Anagata Nusantara (Danantara) has generated extensive public discussion on social media, reflecting diverse perceptions regarding its governance, transparency, and potential contribution to national development. Despite growing public discourse, empirical studies examining public sentiment toward this newly established institution remain limited. This study addresses that gap by analyzing public sentiment toward Danantara using a Support Vector Machine (SVM) classifier with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting, applied to a multi-platform dataset spanning X (Twitter), Instagram, and YouTube an approach that captures a broader and more representative range of public opinion than single-platform studies. A dataset of 3,000 comments was collected and underwent preprocessing, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming, followed by manual labeling into three sentiment classes: positive, negative, and neutral. The proposed LinearSVC model achieved an accuracy of 84.67%, a macro-average F1-score of 0.82, and a weighted-average F1-score of 0.85. Sentiment distribution analysis reveals that negative sentiment dominates public discourse (57.13%), followed by positive (24.37%) and neutral sentiments (18.50%). This study contributes both a novel empirical case being among the first sentiment analyses of public perception toward Danantara and evidence that SVM with TF-IDF offers an effective, robust approach for multiclass sentiment classification of Indonesian social media text, providing actionable insights for policymakers and institutional stakeholders.
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