Pengaruh Modal, Jam Kerja, Jumlah Tenaga Kerja, Jumlah Produksi, dan Penjualan Terhadap Pendapatan Usaha Mikro Kecil dan Menengah Pada Sentra Keripik Khas Lampung di Kedaton Bandar Lampung
Bibliographic record
Abstract
Small and Medium Enterprises (MSMEs) are one of the sectors that are expected to help develop the national economy. this allows it to contribute to efforts to reduce disparities between groups, alleviate poverty, and absorb labor. This study aims to determine the effect of capital, working hours, and number of workers. Total production, and sales to revenue. The object of this research is MSMEs in the typical chips center of Lampung. There are 38 samples in this study, namely kiosk owners who are willing to provide information regarding their financial data, which are processed using SPSS 22. From the results of multiple linear regression tests, it is stated that partially the Sales variable (X5) has a significance of 0.015 less than 0.05 which means it has a significant and significant effect on income, while the variables of capital (X1), working hours (X2), number of workers (X3), and total production (X4) have values greater than 0.05 which means they have no effect on income. The test results simultaneously state that all X variables (capital, working hours, number of workers, total production, sales) have a significance of 0.006 which is smaller than 0.05 which means that it has a significant and significant effect on income. The test results of the coefficient of determination R2 are 0.389, which means that the independent variable has an influence of 38.9% on the dependent variable, while the rest may be influenced by other variables outside the variables in this study.Keyword : MSME, Capital, working hours, number of workers, total production, sales, income
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".