Public Sentiment Analysis of the Agrarian Conflict between PT TPL and the Toba Simalungun Indigenous Community Using the SVM Method - Dian Yusri Andira; Deswita Maharani Harahap; Vibiola Br Damanik; Indah Frian Sari; Victor Asido Elyakim P (Yayasan Literasi Sains Indonesia, 2025)
The agrarian conflict between PT Toba Pulp Lestari and the Toba Simalungun indigenous community has generated diverse public opinions on social media. This study aims to analyze public sentiment regarding the conflict using the Support Vector Machine (SVM) method based on TikTok comment data. A total of 1,751 comments were collected via the TikTok API and processed through cleaning, normalization, stopword removal, and stemming. Sentiment labeling was performed automatically with a lexical-based approach, followed by feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM model was used to classify public sentiment into two main categories, namely positive and negative. The results of the testing showed that the SVM model was able to achieve an accuracy of 80%, with excellent performance in detecting negative sentiment. Additional analysis through wordcloud visualization shows the dominant words in each sentiment category, which reinforces the model's classification results. The findingsof this study provide an objective picture of public opinion patterns on social media, while also demonstrating the potential application of machine learning-based sentiment analysis methods to understand public perceptions of other social issues in the future.