Peningkatan Literasi Digital UMKM dan Karang Taruna melalui Pelatihan Canva untuk Penguatan Branding Potensi Lokal di Kalurahan Pleret
Bibliographic record
Abstract
Transformasi digital menjadi kebutuhan mendesak bagi pelaku UMKM dan organisasi kepemudaan untuk meningkatkan daya saing di era ekonomi berbasis teknologi. Kalurahan Pleret, Kapanewon Panjatan, Kabupaten Kulon Progo, memiliki potensi ekonomi lokal yang besar namun masih menghadapi keterbatasan literasi digital, terutama dalam pemanfaatan media daring untuk promosi. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan peserta dalam menggunakan Canva sebagai platform desain grafis daring sekaligus memperkuat strategi pemasaran digital desa. Metode pelaksanaan mencakup sosialisasi, pelatihan praktik, dan pendampingan yang diikuti oleh 20 peserta dari unsur UMKM dan Karang Taruna. Hasil evaluasi menunjukkan adanya peningkatan keterampilan desain grafis digital sebesar 42,5% berdasarkan perbandingan skor pre-test dan post-test, serta peningkatan aktivitas promosi daring sebesar 65% melalui media sosial dalam dua bulan pascapelatihan. Kegiatan ini tidak hanya meningkatkan kemampuan teknis peserta, tetapi juga menumbuhkan kesadaran kolektif akan pentingnya branding digital desa. Dengan demikian, program ini berkontribusi nyata terhadap penguatan daya saing UMKM lokal dan memperkuat peran Karang Taruna sebagai penggerak literasi digital di tingkat desa.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.021 |
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".