Decision Making Using the MABAC Method to Determine the Leading Small and Medium Industry Centers in Yogyakarta
Bibliographic record
Abstract
Determining small-medium industries (SMIs) centers on producing SMIs capable of developing and excelling in Yogyakarta City. The determination of superior SMIs plays a crucial part in the development of new and creative industries. However, many unresolved SMIs decisions were made manually, thus making the long and ineffective process. Technology entry into various disciplines can make determining superior SMIs more efficient and systematic. To facilitate the determination of superior SMIs, it took advantage of the Decision Support System (DSS), where the author, when carrying out the analysis, applied the Multi-Attributive Border Approximation Area Comparison (MABAC) method for alternative rankings. This research resulted in the ranking of superior SMIs centers, namely alternatives (A6) ranked 1st, (A9) 2nd order, (A2) 3rd order, (A8) 4th order, (A10) 5th order, (A1) 6th order, (A3) 7th order, (A5) 8th order, (A4) 9th order, and (A7) 10th order. The MABAC method was successfully used in the decision-making of superior SMIs centers with a precision of 83.3% and an accuracy of 93.5%, calculated using a confusion matrix. The study’s results discovered that the MABAC approach was successfully used for decision-making for determining superior small and medium industry centers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".