Penerapan Metode Triple Exponential Smoothing Pada Sistem Prediksi Keuntungan Bisnis Ayam Broiler Guna Meningkatkan Pengelolaan Keuangan Peternak
Bibliographic record
Abstract
Profitable business predictions are used to help chicken breeder in anticipating profit earned in the next harvest. The existence of a profitable prediction, enables breeder to predict when the next harvest is experiencing little profit or harvest failures. In addition, to be the breeder still has risky capital and bankruptcy. In this research, the author compare two methods accordingly in this case, there are triple exponential smoothing method and monte carlo method. The data used in the calculation of news data is profitable on the previous harvest. To find the value of two methods are match, the author's use mean absolute percentage error (MAPE) to learn the percentage of the value of the error. Based on the value of MAPE, triple exponential smoothing method have value of 12.10% with α value = 0.3 and monte carlo method have value of 40.58%. Meanwhile with the anticipated value of profit testing for the next 2 harvest grab the difference up to Rp. 19,935,410.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".