Application Of The Ahp Method In Decision Support System For Security Recruitment
Bibliographic record
Abstract
PT Perkebunan Nusantara II (PTPN II) Sei Semayang, a state-owned enterprise (BUMN) engaged in palm oil and sugarcane production. Security officers play a crucial role in maintaining the company's security and productivity. Currently, the security officer recruitment process remains conventional, relying on subjective assessments based on several criteria. The aim of this study is to introduce a more structured, transparent, and objective approach to security officer recruitment. By implementing the Analytic Hierarchy Process (AHP) method, decisions in selecting prospective security officers can be made based on predetermined criterion weights. AHP enables decision-makers to identify, compare, and prioritize relevant criteria, addressing the complexity of selecting candidates who fit the established criteria. Through this approach, the study seeks to enhance efficiency and effectiveness in the security officer selection process, reduce the risk of errors in candidate selection, and improve overall company performance and security. It is hoped that the application of the AHP method in the decision support system for security officer recruitment at PTPN II Sei Semayang will assist the company in optimizing the selection process and supporting more data-driven decision-making.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".