Analisis Sentimen Masyarakat terhadap Kenaikan Harga Bahan Pokok Menggunakan Text Mining dan Pembobotan TF-IDF
-
Raheliya Br Ginting
*
Institut Teknologi dan Bisnis Indonesia, Medan,
Indonesia
- David JM Sembiring Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
- Sinek Mehuli Br Perangin-Angin Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
- Meiliyani Br Ginting Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia (*) Corresponding Author
Abstract
Rising prices for basic commodities represent an economic issue that can affect public purchasing power and trigger various reactions on social media. Given the vast volume of public opinion shared on social media, manual analysis is difficult; therefore, a text-processing approach is required to identify sentiment trends. This study aims to analyze public sentiment regarding the rising prices of basic commodities based on comments posted on Twitter, utilizing a text-mining approach and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. Data were collected through a crawling process using keywords related to the increase in basic commodity prices. A total of 100 entries were gathered, from which 50 were selected for analysis. The processing stages included case folding, tokenizing, filtering, and stemming. Subsequently, specific terms assumed to represent positive or negative sentiments were identified to calculate their term frequency, document frequency, and TF-IDF weights. The results indicate that positive sentiment accounts for 24.241%, while negative sentiment accounts for 75.759%. The prevalence of terms categorized as negative suggests a negative trend within the analyzed data. This study is intended to provide an initial overview of public opinion regarding the rising prices of basic commodities and to serve as supporting information for further research or policy evaluation. However, these findings represent only the analyzed sample and cannot be generalized to the entire population. Furthermore, these percentages are derived from keyword-based calculations and TF-IDF weighting, rather than representing the accuracy of a classification model.
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Copyright (c) 2026 Raheliya Br Ginting, David JM Sembiring, Sinek Mehuli Br Perangin-Angin, Meiliyani Br Ginting
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