How to Use Design Thinking on Trash Bank Process Modeling?
Bibliographic record
Abstract
The development of system model using the traditional System Development Life Cycle often faces big problems. One of the biggest problems is determining the process model. The existing requirements analysis method is not good enough in producing process model. There are many invalid process models, although they have gone through a series of observations on users. To deal with this, a 'soft' or human-centered method is therefore required. Problems are seen and determined from the point of view of the people involved in the problems. One method that can be used to solve problems is the Design Thinking approach. Design Thinking is the process of creating new ideas and innovative approaches that can solve user problems. This paper proposes the use of an alternative Design Thinking approach in conducting a requirements analysis on the development of Trash Bank system with an interactive qualitative approach. The approach taken is to integrate the concept of Design Thinking in the requirements analysis stage. Through collaboration model the Design Thinking to Requirements analysis, the resulting process model is more valid because the process of exploring the requirements becomes deeper, which is based on user experience. The exploration of user experience from Collaboration model will become the basis for process modeling. According to the approach taken, a more humane and more explored prototype of the system flow is obtained from the human attitude that is involved in the process.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".