GAYA KEPEMIMPINAN, DISIPLIN KERJA, DAN KINERJA PEGAWAI
Bibliographic record
Abstract
The leadership style applied by leaders to influence their subordinates has a crucial role in determining organizational success. High work discipline will also make it easier for the organization to achieve its goals. This research aims to determine and analyze the influence of leadership style and work discipline on the performance of Dairi Regency Population and Civil Registration Service employees. At the Dairi Regency Population and Civil Registration Service, several problems were encountered, namely performance that was not optimal, the role of leaders, and employee work discipline that was not optimal. This research aims to analyze the significance of the influence of leadership style and work discipline on employee performance at the Population and Civil Registration Service of Dairi Regency. The research method used is descriptive research with a quantitative approach. The population used in this research were all employees at the Dairi Regency Population and Civil Registration Service, totaling 40 respondents with the sampling technique being saturated sampling with a population size equal to the sample size. The data analysis technique used is multiple regression analysis and hypothesis testing (T Test and F Test) with the help of the SPSS 25 program. The results of this research show that leadership style and work discipline have a positive and simultaneous effect on the performance of Dairi Regency Population and Civil Registration Service employees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".