ANALISIS PERAMALAN (Forecasting) PRODUKSI KARET (Hevea Brasiliensis) DI PT PERKEBUNAN NUSANTARA IX KEBUN SUKAMANGLI KABUPATEN KENDAL
Bibliographic record
Abstract
Rubber plant productivity is affected by production factors such as amount of labour, amount of land area, number of productive tree, manure and rainfall. Production factors must be controlled to meet optimum rubber production, due to the increasing need of rubber. Rubber consumption on 2009 is 9,277 millions ton, while on 2010 increase become 10,664 millions ton. World crude rubber is able to provide 9,702 millions ton on 2009 and 10,219 million ton on 2010. Factor that influence rubber harvest result is the benchmark to get the decision to support the rubber achievement harvest optimally. The purposes of this research is to know and forecasting the harvest result of rubber production in PT Perkebunan Nusantara IX (PTPN IX) Sukamangli estate in the future i.e. 2015, 2016 and 2017. This research used descriptive analytical method. The data analysis used forecasting with ARIMA analysis. The base on ARIMA model, forecasting result for rubber production in 2015 amounted to 325675.9 kg (Quarter I), 396571.3 kg (Quarter II), 338552.1 kg (Quarter III), 258359.4 kg (Quarter IV). In 2016 amounted to 356854.6 kg (Quarter I), 442136.9 kg (Quarter II), 387335.1 kg (Quarter III), 293983.5 kg (Quarter IV). In 2017 amounted to 395750.9 kg (Quarter I), 492849.0 kg (Quarter II), 424360.7 kg (Quarter III), 328790.9 kg (Quarter IV). The result of forecasting rubber production showed that the rubber production while be increases on 2017. Keywords: ARIMA, multiple regression, production factors, rubber production
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".