Reducing Disturbance in Parking System by Using Quality Function Deployment (QFD) Method
Bibliographic record
Abstract
This paper purposed is to apply Quality Function Deployment (QFD) for Parking System Improvement at Taman Bendahara from perspective of customers. The main issue for the QFD problem was from the ‘what’ the customer requirement and ‘how’ to implement the problem to solutions. These two components emphasized on the House of Quality (HOQ) matrices. For this research, a systematic procedure is used in QFD method by applying a factor analysis and correlation Spearman. Factor analysis is the best group identified from the data and reduced the unused items. As for the correlation Spearman, it was used in order to see the relationship and strength of each factor. The result in this research identified four best group criteria which are availability, layout and design, safety and access point. These four criteria indicate the main improvement needed for parking system. By using the QFD method, the management of parking system at Taman Bendahara should listen to the customers’ voice to seek a solution for these issues. This study proposed strategy can be applied for others management to identify the solution for parking problems.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".