Algoritma K Means dan TF-IDF untuk Pengelompokkan Opini Publik terhadap Program Makan Bergizi Gratis pada Komentar TikTok
DOI:
https://doi.org/10.37859/jf.v16i2.11846
Abstract
In Indonesia, 14% of children suffer from stunting due to malnutrition. To address this issue, the government launched the Free Nutritious Meal (MBG) Program, which has generated diverse public opinions on social media, particularly TikTok. This study aims to cluster public opinions regarding the MBG program using the K-Means Clustering algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) without requiring manual labeling. A total of 57,362 comments were collected, of which 53,204 valid comments remained after preprocessing. Truncated Singular Value Decomposition (SVD) was applied for dimensionality reduction, while the optimal number of clusters (K = 5) was determined using the Elbow Method and Silhouette Score. The clustering results identified five main discussion themes: general program discussion (28.4%), child nutrition and school access (7.9%), spontaneous reactive responses (45.2%), criticism and rejection of the program (15.0%), and support for public figures (3.6%). The model achieved a Silhouette Score of 0.0385 and a Davies–Bouldin Index of 4.9507, reflecting the characteristics of short and informal social media text. The findings demonstrate that the unsupervised clustering approach effectively maps public opinion into meaningful thematic groups and provides valuable insights for the National Nutrition Agency to improve menu quality, budget transparency, and distribution standards of the MBG program.
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