Mengukur Efisiensi Investasi Daerah: Analisis Icor Kabupaten Bandung dan Proyeksi Tahun 2025–2030
Bibliographic record
Abstract
This study aims to analyze investment efficiency in Bandung Regency from 2011 to 2024 and project it for the years 2025 to 2030. Investment efficiency is measured using the Incremental Capital-Output Ratio (ICOR) based on data from Gross Regional Domestic Product (PDRB) and Gross Fixed Capital Formation (PMTB) at constant 2010 prices. Forecasting is performed using the Autoregressive Integrated Moving Average (ARIMA) model. The analysis results show fluctuating ICOR values, reflecting annual variations in investment efficiency. Projections for 2025–2030 indicate a potential decline in efficiency, which signals important considerations for regional development planning. The findings highlight the need for the Investment and Integrated One-Stop Service Office (DPMPTSP) to use ICOR as a key performance indicator when formulating more effective and efficient investment policies to support quality economic growth in Bandung Regency. This study recommends improving future investment policies by utilizing the ICOR indicator to monitor and evaluate the effectiveness of regional investments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".