PENENTUAN HARGA POKOK PRODUKSI PADA PT. CENTRAL PROTEINA PRIMA, Tbk
Bibliographic record
Abstract
The rapid growth of the business world is reasonable with fierce competition fornew and similar industries. This phenomenon requires companies in manufacturingto compete competitively as experienced by the animal feed industry, especiallyshrimp feed. Marketing of shrimp feed at the end of 2019 increased after decliningsince the first quarter of the third quarter. According to the head of the aquaculturedivision of the Association of Animal Feed Entrepreneurs (GPMT) Haris Muhtadi,the transmission occurred because of an outbreak of disease attacking shrimp andafter the outbreak ended, shrimp production began to compete again. There aremany ways that companies, especially those engaged in shrimp feed, do so. Startingfrom creating low prices to making brand variations with a certain quality measurethat is used as a price differentiator between these products. To get around this, thecompany must have the right strategy and policy, namely by paying attention to thecost of production of its products. The purpose of determining the cost of goodsmanufactured at PT. Central Proteina Prima, Tbk. This is to analyze the differencein cost of goods manufactured between the methods used by the company and thecost of goods manufactured with the full cost and variable cost methods. This studyuses a qualitative descriptive method and the data source is secondary data. Theresults of the study to determine the cost of production is the shrimp feed factory ofPT. Central Proteina Tbk Medan issued a production cost per kilogram of Rp.14.103.5. Meanwhile, the variable cost of the method according to the previoustheory, the value per kilogram is smaller, namely Rp. 14,049, with a difference ofRp. 54.5 per kilogram. If the company sets a price of Rp 19,745 per kilogram usingthe same method, then determining the cost of goods manufactured 0.5% is moreeffective using the theoretical variable cost method. This difference occurs becauseof the grouping of raw material costs and direct labor costs which affect factoryoverhead costs and the cost of goods manufactured.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.024 |
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".