Proyeksi Produksi Padi Kabupaten Pinrang Dengan Metode Singular Spectrum Analysis
Bibliographic record
Abstract
The increasing population growth in Pinrang Regency every year had an impact on increasing the need for food, especially rice production which was generally a staple food source in Pinrang Regency. so it was necessary to do a forecast to anticipate future food shortages. This study aimed to determine the yield of rice production in Pinrang Regency in 2021 and the level of accuracy of the method used. The results of the study obtained forecast for rice production in Pinrang Regency in 2021 using the Singular Spectrum Analysis (SSA) method, respectively from the first quarter to the third quarter of 33603 tons, 25988 tons, and 43234 tons with the level of forecasting accuracy based on standard MAPE value obtained by 4.97 %. The MAPE value obtained was less than 10 % and close to 0 %, meaning that the SSA method with windows length 9 and 7 groups were very accurate to be used to predict rice production in Pinrang Regency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".