Pemberdayaan UMKM Wilayah Bandongan melalui Sistem Informasi Lazismu Berbasis Web
Bibliographic record
Abstract
Potensi UMKM di wilayah Kecamatan Bandongan Kabupaten Magelang cukup tinggi. Hal ini mendorong Lazismu Bandongan turut berperan serta dalam pemberdayaan dan peningkatan kesejahteraan UMKM yang dituangkan dalam program kerjanya. Aplikasi Mitramu merupakan aplikasi mobile milik Lazismu Bandongan yang digunakan untuk mempermudah para pemilik UMKM di wilayah Magelang khususnya di Bandongan untuk memasarkan hasil produknya. Pemberdayaan UMKM yang dilakukan Lazismu masih terkendala diantaranya data mitra UMKM yang ada di aplikasi Mitramu masih sedikit sehingga informasi potensi UMKM menjadi kurang lengkap. Melalui kegiatan PPMT Universitas Muhammadiyah Magelang bersama Lazismu Bandongan melakukan kegiatan pemberdayaan UMKM. Kegiatan yang dilakukan meliputi pemetaan potensi UMKM dengan melakukan kegiatan survei di beberapa dusun di wilayah Bandongan. Hasil data survei dimuat dalam menu katalog aplikasi Mitramu. Selain itu, untuk mendukung eksistensi Lazismu maka dibuat sistem informasi Lazismu berbasis web yang berfungsi sebagai company profile. Melalui company profile tersebut maka akan memudahkan bagi Lazismu dalam memberikan informasi bagi masyarakat termasuk bagi UMKM sehingga menjadi tidak ragu sebagai mitra Lazismu.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".