ANALISIS DAYA SAING KOPI INDONESIA (STUDI KASUS: EKSPOR KE JERMAN)
Bibliographic record
Abstract
Indonesian coffee industry is one of the products of agriculture which is widely traded in the world market. In fact, more than 18 countries have become Indonesia's coffee export destinations. In the last 20 years, Germany has been in second place as the country with the main coffee export destination after the United States. Studies were carried out in order to know and analyze the level of competitiveness of Indonesian coffee commoditiy to Germany and what factors influence the competitiveness of Indonesian coffee commoditiy to Germany. Sources of data obtained from the Central Statistics Agency (BPS) Indonesia, UN Comtrade and the World Bank. The type of data is a time series with a time period of 21 years, 2000-2020. The research method uses Revealed Comparative Advantage (RCA) to calculate the level of competitiveness then the results of the RCA will be calculated using Multiple Linear Regression (OLS) to find out how much the factors that affect the RCA value. The results of the RCA show that the competitiveness value of Indonesia's coffee commoditiy against Germany has a fairly good value, but is still too far when compared to Vietnam. While the OLS results show that the volume of exports and coffee production has a significant positive value, the rupiah exchange rate is positive but not significant and Germany's GDP is negative but not significant.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".