FEASIBILITY SIMULATION OF HOUSEHOLD SCALE CATFISH RAISING BUSINESS IN THE FIRST QUARTER OF 2022
Bibliographic record
Abstract
The first quarter of 2022 in Indonesia experienced an increase in the prices of basic necessities which could increase the amount of capital needed in a catfish (Clarias sp.) rearing business. Therefore, it is necessary to conduct a feasibility analysis simulation study of catfish rearing business to determine the capital and feasibility of the business. The method used in this study is a literature review or library research, where the data collection method is entirely with secondary data or literature study. Secondary data is collected by reviewing from references in the form of journals, books and online articles that are still related. The calculation of business feasibility analysis is carried out by calculating investment costs, fixed costs, variable costs, calculating production costs, revenues, profits, BEP Rupiah, BEP Units, R/C Ratio and PBP. Furthermore, an analysis of the supporting and inhibiting factors of the business as well as an analysis of the marketing strategy in the catfish rearing business was carried out. The results of the calculation of the business feasibility simulation show that the production cost is Rp. 14,181,228, the revenue is Rp. 24.12 million, a profit of Rp. 9,938,772, Rupiah BEP of Rp. 3,655,160 which means the turning point will be reached if sales reach Rp. 3,655,160, BEP Unit of 185 kg, which means the turning point will be reached if milkfish production reaches 185 kg. R/C Ratio is 1.7 and PBP is 1.65 or 2 cycles where the investment capital for catfish rearing business will return within 6-7 months or 2 cycles. Therefore, the business of raising catfish on a household scale for the first quarter of 2022 can be said to be profitable and feasible to run.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".