Reducing Disturbance in Parking System by Using Quality Function Deployment (QFD) Method
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
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.
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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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