PERENCANAAN DAN PERMODELAN KEBUTUHAN PARKIR UNIVERSITAS SEBAGAI PEMBAHARUAN PEDOMAN PERENCANAAN (STUDI KASUS PUSAT PENDIDIKAN/PERGURUAN TINGGI)
Bibliographic record
Abstract
Guidelines for planning parking facilities have been regulated in the Guidelines for Planning and Operation of Parking Facilities, Director General of Land Transportation but this reference can be said to be past to be used as a guideline, because considering the development of the type, type and number of motorized vehicles growing so rapidly, the size of the unit needs of parking space for each activity center needs to be tested again, in this case, researchers are trying to conduct a survey at the education center / university to provide a foundation which has the potential to be used as a reference for parking planning policies. There were two dependent variables (Y) used in this study: maximum parking accumulation of cars and motorcycles. These two variables were obtained from vehicle surveys conducted using the survey cordon method. However, the independent variable consists of the number of students (X1), the number of lecturers (X2), and the number of education staff (X3). The study used regression analysis, and the SPSS program was used to create and test the model. The results of the analysis obtained the best model for car Y = 29.963 + 0.773 X2 + 0.474 X3 with R2 0.996, for motorcycle Y = 468.577 + 0.380 X1 + -9.608 X3 with R2 0.995. Both models were selected based on significant, simultaneous, normality, linearity, and multicollinearity tests. The results show that both models meet the BLUE criteria, meaning at best, linearity, unbiased, and estimator.
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.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".