Strategi Penguatan Daya Saing Investasi Kota Cilegon melalui Optimalisasi Industri dan Inovasi Pelayanan Publik
Bibliographic record
Abstract
Cilegon City, located in Banten Province, is recognized as a vital industrial hub contributing to Indonesia's economic growth. However, the city's investment competitiveness still requires substantial enhancement to attract both domestic and foreign investors. This study focuses on developing a strategy to strengthen the investment competitiveness of Cilegon City, primarily through optimizing its industrial sectors and innovating public services. The research adopts a descriptive qualitative approach, using literature reviews, interviews, and observations of local policies and conditions to gather data. The findings suggest that improving investment competitiveness can be achieved through the development of industrial clusters, providing necessary supporting infrastructure, and enhancing public service efficiency, particularly through digitalization and innovations in the licensing process. Additionally, fostering collaboration between local governments, businesses, and the community is essential for creating a competitive and sustainable investment environment. Public service innovation, especially in streamlining the licensing process, increases transparency and builds investor confidence. The study concludes that with an integrated strategy, Cilegon City can transform into a top industrial investment destination with the potential to compete effectively on both national and international levels. This research highlights the importance of strategic planning, innovation, and collaborative efforts in positioning Cilegon as a globally competitive industrial center, enhancing its attractiveness to investors and contributing to economic development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".