FAKTOR-FAKTOR YANG MEMENGARUHI EKSPOR DAN PROSPEK EKSPOR PISANG PROVINSI LAMPUNG
Bibliographic record
Abstract
This study aims to determine the factors that influence the exports volume of Lampung Province’s bananas and predict the exports of Lampung Province’s bananas. This research based on the exports of some kind of banana produced by firm and by the corporation program involving the firm and smallholder farmers. This study used both descriptive and quantitative analysis methods and data werw obtained from the relevant authorities as secondary data for 5 years, starting from the first quarter of 2015 to the first quarter of 2020. The data were analyzed by regress time series data using Ordinary Least Square Method (OLS) and Autoregressive Integreted Moving Average (ARIMA). The results of this study showed that the factors that significantly affect the exports volume of Lampung Province’s bananas are domestic banana production and the average of domestic banana price index. The trend of banana exports prospect until the end of 2024 decreases. Key words: banana, exports prospect, Lampung, factor
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".