Penerapan Metode Triple Exponential Smoothing Pada Sistem Prediksi Keuntungan Bisnis Ayam Broiler Guna Meningkatkan Pengelolaan Keuangan Peternak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it