Analysis of Factors Affecting: Sales Volume of Small and Medium Enterprises (SMEs) in Surabaya
Bibliographic record
Abstract
The purpose of this study is to investigate and examine the factors that affect the sales volume SMEs, respondent 101 SMEs obtained that variable Venture Capital (X1) Number of Workers (X2), Hours of Work (X3), Experience (X4) of the Sales Volume (Y), simultaneously influence while variable Production technology and Innovation (X5) and Strategic Marketing (X6) of the Sales Volume (Y) partially has dominant influence. The research instrument used questionnaire, were analyzed using multiple linear regression analysis. The unit of analysis of this research is the management of SMEs Surabaya, who became the target Cooperatives and SME Surabaya in 2013 as many as 315 SMEs engaged in some business sectors such as the following: Handicraft (39), Sewing (16), Handicraft Water Hyacinth (38), ribbon embroidery (44), Processed fish (29), Pastry (50), Wet Cake (50) Beverages (26), crackers (37). In this study took a sample of 101 respondents from various fields of business data analysis contained significant effect on the dependent variable, and the result is expected to be used as one of the considerations in making decisions in developing SMEs.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".