Pemanfaatan Pengetahuan Media Sosial (Online) untuk Edukasi Gizi Menarik dan Inovatif Terhadap Siswa Remaja SMA, SMK serta MA
Bibliographic record
Abstract
Social media has become one of the potential educational tools in improving adolescent nutritional knowledge. This study aims to analyze the use of social media as an interesting and innovative means of nutritional education for high school, vocational school, and Islamic high school students. The method used is a literature review of scientific articles obtained through databases such as Academia.edu, SpringerOpen, Google Scholar, and SINTA. The results of the analysis show that social media-based education, such as Instagram, Facebook and WhatsApp, can significantly improve adolescent knowledge about balanced nutrition. This increase occurs through creative approaches such as the use of videos, comics, and interactive games that can motivate adolescents to understand the importance of nutrition in everyday life. These findings emphasize the importance of utilizing social media as an effective educational tool to support positive behavioral changes related to nutrition in adolescents. Thus, social media-based education strategies can be an innovative step in facing the challenges of adolescent nutrition in the digital era.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".