Analisis Video Views to Followers Ratio Instagram Pada 5 Brand Gaming Gear Lokal Terbaik
Bibliographic record
Abstract
Instagram merupakan salah satu dari bagian komunikasi, yaitu komunikasi visual yang mana komunikasi visual ini dapat membangun citra seseorang, Instagram memiliki 700 juta pengguna aktif secara global. Instagram berasal dari kata “insta” yang mengacu pada kata “instan”, sama halnya seperti kamera polaroid yang mana kamera tersebut menghasilkan foto secara langsung atau instan dan “gram” yang berasal dari kata telegram, dimana telegram berfungsi untuk mengirimkan informasi kepada orang banyak. Dengan banyaknya pengguna Instagram yang ada di Indonesia ini menjadikan kesempatan bagi brand untuk memasarkan produk mereka. Salah satunya adalah brand-brand gaming gear, mereka memasarkan produk mereka di Instagram dengan harapan mendapatkan pelanggan. Adapun 5 Brand Gaming Gear Lokal Terbaik, yang menggunakan Instagram untuk media promosi mereka. Adapun 5 Brand Gaming Gear Lokal Terbaik tersebut adalah: NYK NEMESIS, REXUS ID, Fantech ID, Vortex Series, Digital Aliance. Tujuan dari penelitian ini adalah mengetahui kredibilitas performa dari akun Instagram 5 Gaming Gear Lokal Terbaik menggunakan Video Views to Followers Ratio. Penelitian ini menggunakan metode eksploratif kuantitatif, dan akan menghitung menggunakan rasio-rasio yang ada pada Instagram.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".