Sistem Pendukung Keputusan Pemilihan Pegawai Non-PNS Terbaik di Dinas Pengendalian Penduduk Dan Keluarga Berencana Kota Binjai Menggunakan Metode Moosra Dan Roc
Bibliographic record
Abstract
Employee evaluations within an organization are efforts to measure and motivate employees to enhance their skills and abilities. These evaluations are expected to help the organization assess and identify improvements or developments in employee capabilities to support the organization in achieving its goals. The evaluations are anticipated to produce fair and transparent results that are acceptable to all parties involved. The rapid advancement of information technology has brought changes to the employee evaluation process. Specifically, the Population Control and Family Planning Office of Binjai City requires a system that can assist in evaluating the performance of its non-permanent employees. There are various methods that can be used in designing a system to produce the best decisions. Among them is the MOOSRA (Multi-Objective Optimization on the Basis of Simple Ratio Analysis) method, which is one of the multi-objective optimization techniques used in decision support systems. The MOOSRA method is similar to the MOORA method but differs in performance score determination: MOORA uses a reduction operator, while MOOSRA relies on calculations based on criteria and alternative division operators. MOOSRA performs calculations based on the provided criteria and alternatives. To determine the weight of each criterion, the ROC (Rank Order Centroid) method is employed, which assigns weights to each criterion based on their ranking and priority levels. This ensures that the evaluation of non-permanent employees at the Population Control and Family Planning Office of Binjai City produces the best results. The MOOSRA and ROC methods can be used to build a decision support system that delivers optimal evaluations of non-permanent employees. Awarding the title of "best employee" can enhance morale and motivation among employees in achieving the organization's previously established goals. The selection of the best non-permanent employee must be conducted fairly and transparently so that the results are accepted by all non-permanent employees of the Population Control and Family Planning Office of Binjai City. A decision support system performs calculations based on established criteria for each alternative and provides recommendations that can help leadership make the best decisions. The MOOSRA method is a decision support system technique that uses multi-criteria analysis in its calculations, while the ROC method aids in determining the weight of criteria based on their priority.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".