Analysis of Football Supporters' Sentiment on Social Media on PSSI's Performance using the K-Nearest Neighbor Method
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Bibliographic record
Abstract
The performance of the Football Association of Indonesia (PSSI) often receives public scrutiny, especially from football supporters. The dynamics of Indonesian football, which are frequently colored by controversy, have generated a large number of opinions on social media. This study aims to analyze the sentiment of football supporters on social media regarding PSSI’s performance using the K-Nearest Neighbor (KNN) method. The research data were collected from Twitter through a crawling process, with word weighting performed using the TF-IDF method, while the KNN model was tested with the parameter value of k = 3. The results show that the K-Nearest Neighbor (KNN) model achieved an accuracy of 93.5%, with a precision of 63.2%, recall of 52.9%, and an f1-score of 56.5%. However, the model’s performance was influenced by data imbalance, where neutral sentiment comments were far more dominant than positive or negative ones. The sentiment distribution indicates that public opinion on social media was largely neutral, while the proportion of positive and negative sentiments was relatively smaller. These findings suggest that although criticisms of PSSI’s performance were quite prevalent, most supporters tended to remain neutral in expressing their opinions. Keywords: Sentiment Analysis, K-Nearest Neighbor, PSSI, Twitter
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it