DAMPAK IMBALAN KERJA DAN HARGA POKOK PRODUKSI TERHADAP LABA BRUTO PT. UNILEVER INDONESIA, TBK.
Bibliographic record
Abstract
ABSTRACT The growth of large-scale and medium-scale manufacturing industries in Indonesia today is not yet encouraging and tends to slow down year on year in the first quarter of 2019 and in the 2018 quarter, become down 0.80 percent, due to political temperatures heating up ahead of the presidential election. The rejection of the Omnibus Law made the Company to recalculate the determination of employee benefits which would affect company profits. This study aims to analyze the magnitude of the impact between employee benefits and the cost of good manufacture to the gross profit of PT. Unilever Indonesia, Tbk.. Hypothesis test of this study becomes a reference in establishing the research method. By measuring the dependency between the influencing variables on the affected variable described the mind frame of the model. The analysis technique uses linear regression with the SPSS statistical program. The results of the study are: There is a significant impact between employee benefits on gross profit of 84.90 percent. There is a significant impact between cost of good manufacture on gross profit of 98.70 percent. And simultaneously there is a very significant impact of 99.10 percent between employee benefits and the cost of good manufacture to the gross profit of PT. Unilever Indonesia, Tbk. Keywords: Employee Benefits, Cost of Good Manufacture, and Gross Profit
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.076 |
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".