Sentiment Analysis of Students on Campus Facilities and Infrastructure Using the Naïve Bayes Classifier Method (Case Study STMIK Kaputama)
Bibliographic record
Abstract
Campus facilities and infrastructure play an important role in supporting the quality of learning. STMIK Kaputama faces challenges in maintaining the quality of its facilities as the number of students increases. This study applies sentiment analysis to student comments regarding classrooms, laboratories, libraries, restrooms, parking, and internet access. The method used is the Naïve Bayes Classifier with TF-IDF weighting and text preprocessing, following the CRISP-DM framework. The results show an accuracy of 73%, with the best performance in the positive class with precision 0.72; recall 0.97; F1-score 0.82, while the negative class with precision 0.79; recall 0.38; F1-score 0.51 and the neutral class was not detected. These findings indicate that the model tends to be dominant in positive sentiment but is still weak in distinguishing between negative and neutral comments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".