Pengaruh Fitur Aplikasi Tiktok Jharna Bhagwani terhadap Keputusan Penggunaan Produk Make Up
Bibliographic record
Abstract
As of the first quarter of 2022, there are 1.39 billion monthly active users of the Tiktok app globally. When compared to a year ago, this number is still rising and now stands at 72.17 percent. It was mentioned that there were still 812 million active monthly users in the first quarter of 2021. Video shows are one of the most popular services offered by the Tiktok program since they can motivate users to create films and offer both fun and knowledge. The goal of this study is to determine and quantify how employing Tiktok Jharna Bhagwani application features affects usage decisions. employing a quantitative strategy and an explanatory survey technique. The sample size for this study is the 255 respondents who follow the Tiktok account @JharnaBhagwani, which is only allowed to contain one post. A sample of 72 respondents was created by applying the slovin technique formula to take the sample. The Simple Random Sampling Technique is employed in the sampling process. employing questionnaires for data collecting and simple linear regression analysis for data analysis. The study's findings indicate that the use of the Tiktok application features has a t count value (13.443) > t table (1.667), and that H 0 is rejected and H 1 is accepted, indicating that there is an impact between the use of the Tiktok application features developed by Jharna Bhagwani and the choice to use cosmetics. The utilization of the Tiktok Jharna Bhagwani Application Features, which is the X variable, has an influence on the Y variable, according to the coefficient of determination results, which had a R square value of 0.72. The decision to use makeup products is impacted by other factors outside of this study for 72% and by other factors for the remaining 28%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".