Track Record Model in Employee Performance Optimization Using Weight Product Method
Bibliographic record
Abstract
Employee performance improvement is a crucial aspect for the growth and success of a company, especially in the agricultural sector that relies on the quality and competence of human resources. However, subjective and manual employee assessments often face challenges, such as high levels of subjectivity and the time required to complete the process. To overcome these obstacles, this study proposes the use of the Weighted Product (WP) method as an approach to building a track record model in employee performance assessment. This study involves several methodological stages, first by studying the literature related to decision support systems, WP methods, track records, and employee performance assessments. Furthermore, data collection is carried out from a dataset that includes monthly assessments of employee performance based on several criteria such as attendance, cooperation, work quantity, responsibility, and others. The next process involves modeling, where the WP model is designed to produce the maximum total value of the existing assessment criteria. Model validation is carried out through two approaches, namely the Criterion-related Validity Test and the Internal Consistency Test. The test results show that the WP model has a Criterion-related Validity of 0.9851, indicating a strong relationship between the employee scores generated and the assessments given by the supervisor. In addition, Cronbach's alpha reached a value of 1.0, indicating excellent internal reliability of the model. Thus, the use of the WP method in the employee performance tracking system can be considered effective and can improve objectivity and efficiency in employee performance assessment in the context of agricultural companies. This method not only helps in identifying high-performing employees, but also in motivating them to achieve the highest performance standards, which in turn can improve the overall operational quality and reputation of the company
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".