Application of Apriori to Determine Correlations between Source Competencies Human Resources with Education and Working Period
Bibliographic record
Abstract
Human resource competence (HR) is a key factor in supporting organizational performance. Referring to several problems in the Binjai City BKD, such as there is a difference between the competencies possessed by employees and the competencies needed to carry out their duties and responsibilities effectively, the placement of employees that are not in accordance with the competencies, the absence of a system to map and monitor employee competencies can cause difficulties in identifying development needs, as well as in the placement of appropriate employees. Employee competency mapping is relevant in the implementation of human resource management, both in planning, development and employee placement activities. Therefore, it is necessary to carry out competency mapping that can be used for various human resource management needs and this is in accordance with the priority program that will be carried out in the 2025 RKPD, namely improving the quality of innovative human resources. This study uses a priori algorithm with the Rapidminer application to be able to provide correlation results of human resource competencies. From the results of the research, a correlation was formed between human resource competence and education and work period, namely 8 association rules and the highest best rule was obtained with support of 32% and confidence value of 99.4%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".