Pengaruh Penggunaan Aplikasi TikTok terhadap Literasi Informasi Mahasiswa Jurusan Ilmu Perpustakaan dan Informasi Islam UIN Antasari Banjarmasin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Indonesia is the second most active user of the TikTok application in the first quarter of 2022 because it spends an average of 23.1 hours per month on TikTok. The use of time by the user should not be wasted and can produce benefits in the form of information according to the facts. This research uses participatory media culture theory which explains the ways in which new media culture offers audiences to jointly take on the role of media consumers and media producers at the same time. The media in this research is the TikTok application. The research method is a quantitative research method which aims to show the relationship between the influence of the use of the TikTok application as variable This research includes quantitative data collection, analysis, and statistical testing. With a population of 326 people, a sample of 77 people was taken using the Slovin formula. Data was obtained by distributing closed questionnaires using a Likert scale. The results of the research show that there is an influence of the use of the TikTok application on student information literacy, namely 54.0%, meaning that the influence of the independent variable, namely the use of the TikTok application, on student information literacy is 54.0% while the remainder is (100% - 54.0%) is 46% influenced by other variables outside this research. The R-Square value of 54% is included in the moderate category, meaning that the influence of using the TikTok application on student information literacy depends on the individual user's use of the application.</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it