GROSS REGIONAL DOMESTIC PRODUCT FORECASTS USING TREND ANALYSIS: CASE STUDY OF BANGKA BELITUNG PROVINCE
Bibliographic record
Abstract
Gross Regional Domestic Product (GRDP) is one of the important indicators to determine the economic conditions in the region. This study aims to forecast the Gross Regional Domestic Product (GRDP) of the Province of Bangka Belitung Islands which is dominated by tourism sector. This forecasting to be expected to give information to formulate a type of policy action that will be conducted by decision makers based on GRDP data. GRDP data are from the first quarter of 2010 to the fourth quarter of 2017 on the basis of constant prices in 2010. Data sources are obtained from Central Bureau of Statistics (BPS) of the Province of Bangka Belitung Islands. The forecasting method used is the research is trend analysis. The results of the GRDP forecasting of Bangka Belitung Province in the first quarter of 2018 to the fourth quarter of 2022 shows an increasing trend. It can be seen from historical data that shows an increasing trend as evidenced from the graph on linear trends. The increasing trend in GRDP of the Bangka Belitung Islands Province for the next five years is supported by government policies that prioritize the tourism sector. Consequently, by prioritizing the tourism sector, this will increase economic growth and can reduce GRDP dependency on mining sector, especially tin that has been continuously decreased.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".