A Cost-Driven Method for Determining the Optimum Selling Price in Tofu Production on the Household-Scale Tofu Agroindustry: A Case Study in Mataram, Indonesia
Bibliographic record
Abstract
The determination of the cost of production is a necessity for every entrepreneur to establish the cost of the goods sold. The methods commonly used are the Cost Structure method, the Activity-Based Costing method, and the Volume Cost Profit method. The solution proposed in this article is a cost-driven method which is considered simpler in determining the optimal selling price for the tofu agroindustry on a household scale. This study aims to analyze the correlational relationship between raw material costs, firewood costs and labor wages with production, and to find a cost-driven formulation by modifying the Volume Cost Profit method. The research was conducted in the tofu agro-industry center in Kekalik Jaya Village, Sekarbela District and in Abianbadan Baru Village, Sandubaya District with the number of respondents 40 units of tofu agro-industry selected by the accidental proportional sampling method consisting of 27 agro-industry units in Kekalik Jaya sub-district and 13 agro-industry units in Abianbadan Baru village. Collecting data using triangulation methods, namely the method of sending questionnaires to respondents, survey methods with direct interviews with tofu agro-industry business actors, and observation methods at the production process site. The results showed that there was a positive correlation between the cost of raw material for soybean seeds and production and was the largest component of production costs so that it could be used as a cost determinant in the processing of soybeans into tofu, the cost driven method could be used as strategic planning in determining the selling price.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".