Sentiment Analysis of Students on Campus Facilities and Infrastructure Using the Naïve Bayes Classifier Method (Case Study STMIK Kaputama)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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