EFEKTIFITAS PROMOSI DESTINASI WISATA REKREASI GUNUNG PANCAR MELALUI POSTINGAN INSTAGRAM MEDIA SOSIAL
Bibliographic record
Abstract
Penggunaan media sosial belakangan menjadi banyak diminati oleh masyarakat sebagai sarana untuk berkomunikasi, seperti Instagram yang merupakan media yang sering digunakan oleh penggunanya sebagai media untuk memposting beragam gambar, pada saat ini banyak sekali pengguna instagram yang menjadikan nya sebagai salah satu media promosi. Media promosi ini juga digunakan oleh owner di gunung pancar untuk menarik wisatawan. Dan menariknya berkembangnya sektor pariwisata alam Gunung pancar menjadi penunjang ekonomi bagii masyarakat yang ikut andil dalam pemeliharaan wisata tersebut dan khususnya penunjang ekonomi di wilayah Kabupaten Bogor. Tujuan penelitian ini Untuk mengetahui efektifitas promosi destinasi wisata rekreasi gunung pancar melalui postingan Instagram. Adapun hasil dari penelitian menunjukkan bahwa promosi melalui instagram sebagai media sosial yang cenderung memiliki banyak penggunanya sehingga dirasa efektif untuk dijadikan media baru untuk melakukan promosi pemasaran.
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 | 0.002 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".