Sentiment Analysis of Indonesian Nickel Downstreaming on X Using Naïve Bayes and K-Nearest Neighbors
Abstrak
Nickel downstreaming has become one of Indonesia’s most prominent industrial policies, positioned as a pathway to economic growth and global relevance in the electric vehicle supply chain. Despite its ambitions, the policy has triggered intense debate on social media, where concerns about ecological damage and foreign dominance intersect with narratives of national pride. This study employs sentiment analysis to examine public perceptions of the policy through 337 tweets collected from X (formerly Twitter). Two machine learning algorithms, Naïve Bayes and K-Nearest Neighbors, were applied to classify sentiment into positive, negative, and neutral categories, followed by evaluation using confusion matrices, accuracy, precision, recall, and F1-score. The results show that negative sentiment dominates across both models, with Naïve Bayes achieving higher accuracy and recall, while KNN displayed strengths in precision and F1-score. Wordcloud analysis further revealed that positive sentiment is associated with industrial progress and national identity, negative sentiment emphasizes environmental risks and foreign control, and neutral sentiment reflects factual reporting of events. These findings confirm that nickel downstreaming remains a contested policy, viewed as an economic opportunity by some and as a source of social and ecological concern by many others. This study demonstrates the value of integrating sentiment analysis with policy research, as social media provides real-time insights into how citizens perceive government initiatives. The evidence highlights the importance of addressing environmental sustainability and equitable resource management to build trust and legitimacy. Sentiment analysis therefore serves not only as a tool for understanding public opinion but also as a guide for shaping more inclusive governance.
Identifier
- DOI: https://doi.org/10.33093/ijoras.2025.7.3.5
- OpenAlex: https://openalex.org/W7113897600