Analysis of Digital Supply Chain Management in the Development of Export Products for Rattan Crafts in Cirebon
Bibliographic record
Abstract
This research is motivated by the fact that in Cirebon West Java, which is the largestrattan craftsman area in Indonesia, was found that there are many unresolved obstaclesin the development of rattan craft products that will be exported to several countries.The obstacles include the supply chain or the availability of raw materials, the lackof capital for small and medium industry (IKM) stakeholders, and the lack of humanresources management (HRM) as a form of regeneration for rattan craft craftsmen.This study used a qualitative approach where data were collected through observation,documentation, and interviews. The results of this study show that the problems facedby rattan handicraft producers are: 1) a lack of availability of raw materials caused bylong shipping distances, such as from Sulawesi and Kalimantan to Cirebon; 2) a lackof capital that can be absorbed by small entrepreneurs to continue to be able to run thisrattan craft business because of the lack of attention given to small entrepreneurs fromboth the government of Cirebon and the World Bank. 3) the lack of human resourcemanagement for rattan handicraft producers due to the lack of interest from the youngergeneration and the lack of a training platform for the younger generation to continuethis rattan craft relay from time to time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".