ANALISIS OPTIMASI PENGGUNAAN FAKTOR PRODUKSI KOPI BUBUK PADA AGROINDUSTRI XYZ DI KOTA JAMBI
Bibliographic record
Abstract
This research aims to determine the influence of the factors of production of raw materials (coffee beans) and labor in the coffee ground agroindustry in Jambi City, analyze the level of optimization or economic efficiency of the use of coffee bean production factors and labor in the coffee ground agroindustry and determine the use of production factors optimal coffee beans and labor in the coffee ground agroindustry. This research was conducted for six (6) months from April to September 2019. The research subjects were the owner of the ground coffee business and the object of research was ground coffee produced in various types of packaging. 1 month, starting June 2019 until July 2019. Based on the research results physical/technical addition of raw materials for coffee beans and labor per quarter influences the increase in ground coffee production. Each additional raw material for coffee beans by 10% will increase the production of ground coffee by 4.09%, while the addition of labor will increase the production of ground coffee by 4.14%. The use of coffee bean raw material production factors can be added up to an optimal limit of 28,876.35 kg/quarter or there is an increase of 23.68% of the actual use. In one month the optimal limit is the amount of raw material used to be 9,625.45 kg or 385.02 kg per day. The use of labor can be added up to an optimal limit of 114 working people (HOK) or an increase of 25.30% of the actual use of labor. Calculated in working hours, the optimal use of working hours is 17,100 working hours per quarter or 5,700 working hours per month. Within one workday the limit for the optimal use of work is 228 hours with the number of workers being ± 38 people. The optimum amount of production is 27,354.32 / quarter, an increase of 5,165.02 kg (23.28%) of actual production. The optimum profit increased by Rp 335,257,566.67 or 23.68% of the actual profit.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".