Motion Graphic Iklan Layanan Masyarakat Edukasi Tata Tertib Rambu Lalu Lintas
Bibliographic record
Abstract
Iklan layanan masyarakat ialah iklan yang menyajikan pesan-pesan sosial yang bertujuan untuk membangkitkan kepedulian masyarakat terhadap sejumlah masalah yang harus mereka hadapi, yakni kondisi yang bisa mengancam keselarasan dan kehidupan umum. Kurangnya kesadaran oleh pengendara sepeda motor seringkali berakibat fatal terhadap dirinya dan bahkan orang lain. Bahkan banyak dari pengendara sepeda motor melanggar peraturan yang telah ditetapkan yang bertujuan untuk ketertiban berlalu lintas di jalan raya. Sehingga dibuatlah penelitian untuk melakukan produksi video motion graphic iklan layanan masyarakat mengenai edukasi tata tertib rambu lalu lintas menggunakan metode penelitian Kuantitatif dengan metode penyelesaian vaughan. Dan pada hasil analisis yang dilakukan terlihat hasil nya pada tingkat ke efektifitas yang berdasarkan interval skala likert pada ahli diperoleh predikat “Sangat Setuju” dengan nilai rata- rata 96,25% (berdasarkan rincian aspek : Grafis 90%, Tipografi 95%, animasi 100% dan audio 100%). Dan pada perhitungan EPIC Rate didapati sebesar 4.145 (berdasarkan rincian aspek : Empathy 4.19, Persuation 4.16, Impact 4.17, dan Communication 4.06 dan berdasarkan pertanyaan mengenai kinetic typografi yang sudah di masukkan pada kuisioner membuktikan bahwa penggunaan kinetic typografi efektif untuk dilakukan
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".