Simple Additive Weighting Untuk Penentuan Target Pasar
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 0.286 |
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