POTENSI DIGITAL EKONOMI BAGI PERKEMBANGAN SEKTOR PARIWISATA DI INDONESIA
Bibliographic record
Abstract
Perkembangan ekonomi digital pada Indonesia sudah mengalami kemajuan yang sangat pesat salah satunya pada bidang pariwisata. Kemajuan ekonomi digital dibidang pariwisata bisa menarik wisata nusantara (winus) juga manca negara berkunjung ketempat wisata Indonesia. Dampak menurut kemajuan ekonomi digital mengangkat poly bisnis mini & menengah buat memasuki usaha dunia. Pertumbuhan ekonomi Indonesia yang relatif tinggi layanan kepariwisataan Indonesia masih sedikit layanan digital yang dikembangkan. Dari analisa perkembangan layanan digital dibidang pariwisata memakai method forecasting menurut data yg diambil bisa memprediksi 10 tahun ditahun 2028 pengeluaran 465,1 Triliun rupiah & bepergian 425,75 juta kali bepergian bila dibandingkan menurut tahun 2018 dalam pengeluaran 291,09 Trilium rupiah & bepergian hanya 303,4 juta kali bepergian, menurut analisa yg didapat mempunyai peluang bagi teknopreneur buat menciptakan star up dibidang pariwisata yg menjembatani atara pariwiswata & wisatawan nusantara tentu menguntungkan bagi pertumbuhan ekonomi Indonesia menggunakan memakai digital ekonomi pada era industri ekonomi digital ini.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".