OPTIMALISASI PEMASARAN DIGITAL BERBASIS MEDIA SOSIAL UNTUK MENINGKATKAN PENJUALAN UMKM
Bibliographic record
Abstract
Abstrak: Pandemi Covid-19 mendorong pelaku bisnis untuk mengadopsi strategi pemasaran digital berbasis media sosial, termasuk UMKM. UMKM Jejukutan merupakan salah satu usaha yang belum memanfaatkan penggunaan pemasaran digital secara maksimal. Akibatnya, realisasi penjualan belum mampu memenuhi target penjualan meskipun tren penjualan terus mengalami peningkatan. Kegiatan pengabdian masyarakat ini bertujuan untuk mengoptimalkan peran pemasaran digital melalui Facebook dan Instagram sehingga dapat meningkatkan jumlah transaksi dan penjualan produk. Kegiatan pengabdian ini melibatkan tiga orang pemilik UMKM Jejukutan. Hasil monitoring dan evaluasi menunjukkan bahwa Pelaksanaan kegiatan menggunakan metode wawancara dan diskusi, pendampingan, serta monitoring dan evaluasi. mengindikasikan bahwa program optimalisasi pemasaran digital pada Facebook dan Instagram secara efektif meningkatkan jumlah transaksi pelanggan sebanyak 32,71% dan meningkatkan penjualan produk UMKM sebesar 39,49%.Abstract: The Covid-19 pandemic has encouraged business people to adopt social media-based digital marketing strategies, including MSMEs. Jejukutan SMEs is one of the businesses that have not utilized digital marketing to its full potential. As a result, the realization of sales has not been able to meet the sales target even though the sales trend continues to increase. This community service activity aims to optimize the role of digital marketing through Facebook and Instagram to increase customer transaction and sales of products. This service activity involves three Jejukutan MSME owners. The monitoring and evaluation results show that optimizing digital marketing on Facebook and Instagram effectively increases the number of customer transactions by 32.71% and increase sales of MSME products by 39.49%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".