Optimalisasi Digital Marketing dengan tambahan Kosa kata Penjualan Berbahasa Inggris Griya Matahari Desa Purwokerto
Bibliographic record
Abstract
Melemahnya perekonomian Indonesia sejak pandemi COVID-19 mengakibatkan banyak pelaku UMKM yang mengalami penurunan. Oleh karena itu, pemulihan ekonomi berbasis teknologi sangat diperlukan untuk meningkatkan perekonomian. Dengan adanya teknologi dan pemahaman kosa berbahasa Inggris yang tepat, dapat memudahkan pemasaran produk UMKM dari konvensional menjadi digital dengan berbagai kelebihan menggunakan Digital Marketing. Tujuan dari pelatihan ini adalah untuk mengoptimalkan pemasaran produk rajutan Griya Matahari Desa Purwokerto, Kecamatan Srengat, Kabupaten Blitar. Untuk itu, penulis menggunakan 4 teknik (perencanaan, pelaksanaan, observasi, dan evaluasi) untuk memecahkan masalah UMKM Griya Matahari. Dengan pelatihan ini, UMKM Griya Matahari dapat memiliki pengetahuan yang lebih mengenai Digital Marketing sehingga dapat meningkatkan penjualan produk UMKM tersebut.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 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".