Analysis of Student Satisfaction Sentiment Towards Lecturer Performance using the Naive 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
Student satisfaction with lecturer performance is an essential indicator in assessing the quality and competitiveness of higher education institutions. This study aims to analyze student sentiment regarding lecturer performance using the Naïve Bayes Classifier method. The research data were collected from student satisfaction surveys conducted during the 2022–2025 academic years, consisting of open-ended comments about lecturer performance. The research follows the CRISP-DM methodology, including text preprocessing (case folding, cleaning, tokenizing, stopword removal, normalization, and stemming), word weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, and sentiment classification into positive, negative, and neutral categories using the Naïve Bayes Classifier algorithm. The implementation was carried out using the Python programming language through Google Colab. The results show that the Naïve Bayes Classifier model achieved an accuracy of 76.9% in classifying student opinions, providing a reliable representation of students’ perceptions. These findings are expected to serve as a basis for evaluation and strategic decision-making to improve teaching quality and lecturer performance in higher education institutions.
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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 | 0.004 |
| Science and technology studies | 0.001 | 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