PERKEMBANGAN INDIKATOR MAKRO EKONOMI KOTA BANDUNG KUARTA II-2019
Bibliographic record
Abstract
Analysis of the development of macroeconomic indicators is commonly done as an evaluation material and economic management strategy in the future. There are 3 macroeconomic indicators that are commonly used at the regional level, namely the rate of inflation, economic growth and employment. This study aims to conduct an analysis of the three indicators at the Bandung City level. The research method used is descriptive and quantitative analysis. Descriptive analysis is based on the movement of data presented through graphs and tables. While quantitative analysis is more focused on calculating the inflation variable projections and correlation analysis between macroeconomic variables. Based on the results of descriptive analysis, it is known that the Bandung City inflation indicator was relatively maintained in the second quarter of 2019. Even though there is a moment of Eid, the inflation rate is still under control, and even tends to decrease in June 2019. However, the inflation rate is predicted to increase in the third quarter of 2019. Food commodity inflation is predicted to occur due to the peak of the dry season in August-September. On the indicator of the rate of economic growth, the most recent data for 2017 shows a decline in the growth rate, by 7.21%. The achievement of economic growth is also the lowest since 2011. On the employment indicator, there is an irrelevant relationship between the Economic Growth Rate (LPE) Labor Force Participation Rate (TPAK), and the Open Growth Rate
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 0.003 |
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".