ANALISIS BEAUTY VLOGGER, PRODUK HALAL DAN CITRA MEREK TERHADAP MINAT BELI KOSMETIK MUSLIMAH DI INDONESIA
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengatahui Analisis Beauty Vlogger, Produk Halal, Dan Citra Merek Terhadap Minat Beli Kosmetik Muslimah di Indonesia. Penelitian ini termasuk jenis penelitian kuantitatif dengan pengujian hipotesis. Teknik pengambilan sampel menggunakan non probability sampling. Adapun teknik sampling yang digunakan dalam penelitian ini yaitu purposive sampling. Purposive sampling adalah teknik penentuan sampel dengan pertimbangan tertentu. Berdasarkan teknik sampling tersebut maka karakteristik responden dalam penelitian ini yaitu viewers yang pernah atau sering menonton beauty vlog Tasya Farasya dari berbagai sosial media seperti Instagram atau YouTube dan serta menggunakan produk kecantikan yang berlabel halal. Alat analisis yang digunakan dalam penelitian ini yaitu teknik analisis alur (Path Analysis) dengan bantuan Software untuk sistem operasi yang bernama Smart-PLS (Partial Least Square) Versi 3.0. Hasil penelitian membuktikan bahwa Beauty Vlogger Berpengaruh Terhadap Minat Beli, Produk Halal Berpengaruh Terhadap Minat Beli, Citra Merek Berpengaruh Terhadap Minat Beli.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".