DESAIN MEJA SETRIKA LIPAT UNTUK UMKM LAUNDRY DENGAN METODE QUALITY FUNCTION DEPLOYMENT
Bibliographic record
Abstract
The growth of laundry every year will increase by 20% and reach 50% in 2022. In the laundry washing process, the process of ironing and folding clothes is a process that requires the most energy and long time. Constraints that occur in the process of ironing and folding clothes cause overload so that many laundries refuse customer requests causing the resulting turnover to be not optimal. The folding ironing table innovation can combine the functions of ironing as well as folding clothes in one process. The Quality Function Deployment (QFD) method is used to get the voice of the customer and build the House of Quality (HOQ) matrix. Customers desire an ironing board product that has strong and sturdy material, ergonomic design, wide table size, flexible and multifunctional, quality base, and has an attractive design. The development of a folding ironing table product design with a strong and sturdy material, specifically stainless steel, as well as an ironing table base that can be rotated and folded flexibly is expected to make the process of ironing and folding clothes more effectiveKeywords : laundry, ironing, folding clothes, Quality Function Deployment, House of Quality
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".