Peningkatan Perekonomian Umkm Desa Puhjajar Kecamatan Papar Kabupaten Kediri Melalui Penerapan Inovasi Mesin Perajang Lontongan Kerupuk Otomatis
Bibliographic record
Abstract
Puhjajar is one of the villages in Papar District, Kediri Regency with the majority of the population making their living as producers of realizeyah crackers. They are members of associations, each of which consists of 11 craftsmen, where each craftsman has four to six members. The realizeyah crackers produced by the people of Puhjajar village are very famous among the cracker industry, especially in Kediri. In fact, in the marketing of these realizeyah crackers, they are sent outside Java and overseas. However, the problem arises as the market for realizeyah cracker products increases, namely that the realizeyah cracker producers have not been able to increase their production capacity. So there are often delays in delivery which can result in disappointing consumers. This happens because the production process is quite long, and raw materials are sometimes difficult to obtain. It was from this problem that the Nusantara University PGRI Kediri community service team tried to help solve the problems faced by producers by designing an automatic cracker cracker chopping machine. The results obtained after using this machine were an increase in production capacity, which initially only reached 80kg/day to around 350kg/day. Manufacturers are of course greatly helped by the existence of this chopping machine, they are ultimately able to meet the quite high market demand.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
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".