Pendampingan Pemasaran Produk UMKM Slow Food Menggunakan Sosial Media Facebook
Bibliographic record
Abstract
The livelihoods of most residents in Lebak district are rainfed rice farming, gardening, in addition to raising goats. Many of the plantation commodities that generate income for the villagers in Lebak Regency include plantation products such as rubber, oil palm, cocoa, robusta coffee, sugar palm, cloves, coconut, hybrid coconut, pepper, pandan, tea, cashew nuts, vanilla, jatropha, jatropha, and Kapok. In addition, the difficulty of transportation in some areas of Lebak Regency makes it difficult for farmers to market their products. Electricity in Lebak is supplied by PLN continuously, but there are still some areas that have not been touched by electricity at all, so the residents still rely on oil lamps as a means of lighting at night. Phone and GPRS signal is still difficult. This condition is what causes the marketing of products produced by farmers or MSME actors is still marketed in their respective regions and even the barter process is still going on (exchanging goods). During the Covid-19 Pandemic, Slow Food MSME actors experienced a decline in sales due to a decline in people's purchasing power which was the effect of the covid-19 pandemic. From some of these problems, community service will provide training and socialization of the use of information technology, namely by providing knowledge on the use of Facebook social media as a means of marketing MSME products, with the aim that Slow Food MSME business actors can continue to market their products and increase people's purchasing power towards Slow Food MSME products. It is hoped that after participating in the training and socialization, Slow Food MSME business actors have an online buying and selling account through Facebook that is easy, effective, and safe so that they can increase product marketing not only in their own region, but also on a national and international scale.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".