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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.015 |
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; both teacher heads agree on what is shown here.
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".