ANALISIS TREN PESERTA DANA PENSIUN DI INDONESIA
Bibliographic record
Abstract
Indonesia ranks 7th globally in terms of Purchasing Power Parity (PPP) and 17th in nominal GDP, recording 5.02% year-on-year growth in the fourth quarter of 2024. Despite this positive performance, pension fund participation has remained stagnant since the enactment of Law No. 11/1992. Data from the Financial Services Authority (OJK) show that pension benefit liabilities increased by 4.6% (YoY) to IDR 393.52 trillion, while participant contribution growth slowed to 1.92% (YoY). This study applies trend analysis using trend models within a time series framework to forecast the growth of pension fund participants. The exponential trend model was found to be the most accurate, with the lowest Standard Error of the Estimate. Projections indicate an increase in participants for the Financial Institution Pension Fund (DPLK) and a decline for the Employer Pension Fund (DPPK). These results provide insights for stakeholders to formulate strategies that enhance pension fund participation and ensure long-term benefit sustainability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".