Recruitment of STMIK Kaputama Laboratory Assistant with the Waspas method
Bibliographic record
Abstract
Decision Support System (DSS) is one of the approaches that can be used in the selection process for accepting laboratory assistants in a tertiary institution. DSS is often used to assist various decision-making processes within an organization. Through the various stages contained in the DSS, it is able to produce the output in the form of the best alternative from the various criteria that have been determined by the decision maker. There are various methods that can be used in relation to DSS, one of which is the Weighted Aggregated Sum Product Assessment (WASPAS). The Decision Support System can speed up the recruitment of new Laboratory assistants according to predetermined criteria when recruiting prospective Laboratory assistants at STMIK Kaputama. The STMIK Kaputama Laboratory is a computer laboratory that is used to support practicum courses at STMIK Kaputama. Each course has at least one assistant. The requirements for prospective laboratory assistants are that they must register and meet the criteria as potential assistants. The results of this study indicate that the proposed model can be used properly in carrying out the selection process for laboratory assistant recruitment. the WASPAS method is able to produce decisions in the form of the best alternative that can be used to assist decision-making parties. so that it can determine who is eligible to be accepted as a computer laboratory assistant at STMIK Kaputama.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".