Popularitas Empat Destinasi Wisata Pulau Terbaik Dunia Menggunakan Google Trends
Bibliographic record
Abstract
Tujuan: Penelitian ini dilakukan untuk menganalisis popularitas empat destinasi wisata pulau di dunia yaitu Phuket, Bali, Hawaii, dan Langkawi. Metode penelitian: Penelitian ini menggunakan alat analisis statistik deskriptif dengan bantuan Google Trends untuk menentukan popularitas empat destinasi pulau tersebut. Hasil dan pembahasan: Analisis menunjukkan bahwa sejak awal tahun 2000 hingga akhir tahun 2021, destinasi Phuket sangat populer bagi wisatawan yang berasal dari Thailand sendiri, Russia, Turkey, Hong Kong, dan Singapore. Bali sangat populer di kalangan wisatawan Indonesia sendiri, Netherlands, India, Australia, dan Belgium. Hawaii sangat populer di kalangan wisatawan dari United States sendiri, Japan, Canada, Brazil, dan South Korea. Langkawi sangat populer di kalangan wisatawan yang berasal dari Malaysia sendiri, Singapore, Pakistan, Hong Kong, dan Egypt. Implikasi: Destinasi wisata yang paling populer adalah Hawaii, kemudian Bali, lalu Phuket, dan yang terakhir adalah Langkawi. Bali menduduki posisi kedua atau setelah Hawaii.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".