Application of Data Mining to Measure the Level of Satisfaction with Public Facilities and Services at STMIK Kaputama Binjai Using Linear Regression Method
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to analyze the level of satisfaction of STMIK Kaputama Binjai students with physical facilities (classrooms, laboratories, prayer rooms, wifi) and general services (administration, academic guidance, library, security, campus cleanliness) using multiple linear regression methods. Data were collected through questionnaires from students in the 2022/2023 academic year. The results showed that both variables have a significant effect on student satisfaction, with a regression coefficient of physical facilities of 0.40 and general services of 0.59, indicating that general services have a greater impact. Prediction of student satisfaction reached an accuracy level of 98% with a Mean Absolute Percentage Error (MAPE) value of 2%. Laboratory facilities and internet access (wifi) are the dominant factors affecting satisfaction. Based on these findings, improvements in both aspects are recommended to increase student satisfaction and institutional competitiveness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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