Analisis Image Comments to Image Likes Ratio Pada 5 Brand di Indonesia Yang Sering Dikira Brand Luar Negeri
Bibliographic record
Abstract
Instagram merupakan aplikasi sosial media yang berasal dari Amerika yang dibuat oleh Kevin Systrom dan Mike Krieger. Instagram memungkinkan penggunanya membuat video berdurasi 15 detik sampai dengan 15 menit yang disertai dengan musik, filter, dan beberapa fitur kreatif lainnya. Di Indonesia terdapat 59,840 juta pengguna Instagram, dengan jumlah sebanyak itu maka 22,6 persen penduduk di Indonesia merupakan pengguna Instagram. Maraknya jumlah pengguna Instagram yang aktif di Indonesia tentu dapat memberikan peluang bagi brand untuk menjadikan platform Instagram sebagai platform social media marketing. Adapun 5 Brand di Indonesia Yang Sering Dikira Brand Luar Negeri yang memanfaatkan Instagram sebagai platform marketing, yaitu : J-CO, CFC, Krisbow, Men’s Republic, dan Polytron. Tujuan dari penelitian ini yaitu untuk menghitung kredibilitas dari performa akun Instagram Top 5 Brand di Indonesia Yang Sering Dikira Brand Luar Negeri. Metode yang digunakan untuk penelitian ini yaitu metode eksploratif kuantitatif. Hasil dari penelitian ini menunjukan bahwa Brand J-CO Indonesia mendapatkan peringkat pertama dan memiliki kredibilitas performa akun yang baik.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".