Metode Fuzzy TOPSIS Sebagai Sistem Pendukung Keputusan dalam Menentukan Pegawai Berprestasi
Bibliographic record
Abstract
Fuzzy TOPSIS as a decision support system is a mathematical method with the best alternative concept chosen not only to have the shortest distance to the positive ideal solution, but also to have the longest distance to the negative ideal solution. The use of Fuzzy TOPSIS as a decision support system can minimize the weaknesses that exist in the TOPSIS method. The purpose of this study is to apply the Fuzzy TOPSIS method as a Decision Support System (SPK) to determine outstanding employees at the Batu City Population and Civil Registration Office. The Human Resources (HR) Division has the task of validating the value and processing the value of employee work goals and work behavior using the Fuzzy TOPSIS implementing into recommendations for outstanding employees. Data processing is carried out fuzzy, while calculations are carried out by the TOPSIS method. The output of this calculation is in the form of ranking the value of preferences and recommendations available for all employees. The calculation results obtained the highest preference value, namely by the first alternative with a value of 1. The alternative occupied a position as an outstanding employee at the Batu City Population and Civil Registration Office.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".