Simple Additive Weighting Untuk Penentuan Target Pasar
Bibliographic record
Abstract
Changes in information and communication technology have encouraged the formation of an information society. One of the strategic elements for business organizations is processing data quickly and accurately for decision making. Therefore we need a computer-based decision support system that can support the company's decision-making process quickly and accurately. Currently, Glints Talenthub Batam still uses manual methods for decision making in determining the target market, so it takes a long time and results are less accurate. Based on this, the authors try to develop a computer-based decision support system with the Simple Additive Weighting (SAW) method to assist the decision-making process in determining the target market at Glints Talenthub Batam. The results of this study are useful for getting a faster and more accurate decision on which target market to take at Glints Talenthub Batam. In this case, the best target market decision to make is Canada and the United States (San Francisco) ranking first with a final score of 18.68, followed by the United Kingdom in the next rank with a final score of 18.35.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".