Analisis Berbagai Faktor Yang Mempengaruhi Efektivitas Periklanan Produk Sarung Wadimor
Bibliographic record
Abstract
This research have done at PT Sukorintex Kabupaten Batang. advertising messages, advertising creativity, endorser's credibility, and the frequency of display of advertisements are factors that can affect the advertising effectiveness of Wadimor sarong products. This study aims to analyze the influence of advertising messages, advertising creativity, endorser credibility, and frequency of advertising on the effectiveness of advertising of Wadimor products. The type of data in this study is quantitative, primary data that has been distributed to respondents using a questionnaire that is determined using purposive sampling method by drawing samples based on research objectives and predetermined criteria of 100 people and returning 100 questionnaires. The analytical tools used in this research are validity test, reliability test, classical assumption test, multiple linear regression analysis. The results of the analysis show that the advertising message, endorser's credibility does not affect the advertising effectiveness of Wadimo products. Advertising creativity and frequency of advertising display have an effect on the effectiveness of advertising of Wadimor product
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".