STRATEGI PENGUATAN INFRASTRUKTUR DAN PENGEMBANGAN KAWASAN INDUSTRI UNTUK MENDORONG INVESTASI DI KABUPATEN INDRAGIRI HILIR
Bibliographic record
Abstract
The economic growth of Indragiri Hilir Regency in 2024 was recorded at 3.10%, while the regional investment growth in 2024 reached 6.79%, showing an improvement compared to -0.56% in 2023. However, this increase in investment realization has not yet had a significant impact on the economic growth of Indragiri Hilir Regency. This is due to several key issues, including dependence on the agricultural and processing industries, a decline in agricultural production, and limitations in infrastructure and accessibility. Using the USG method (Urgency, Seriousness, Growth), the main issue addressed in this policy paper is Infrastructure and Accessibility Constraints. The problem statement identified is:"The limitations in infrastructure and accessibility in Indragiri Hilir Regency are caused by the lack of investment-supporting infrastructure, which restricts the development of industrial zones in the region. This, in turn, hampers investment flows, increases logistics costs, and slows down regional economic growth." To address these challenges, the Indragiri Hilir Regency Government must take strategic steps, including: Development of Economic and Industrial Zones, Strengthening Physical Infrastructure, Digitalization of Economic Infrastructure, Collaboration and Partnerships, Pro-Investment Regulations and Policies. As these steps cannot be implemented simultaneously, they are divided into three programs: Short-term program, Medium-term program, Long-term program. Pertumbuhan ekonomi Kabupaten Indragiri Hilir Tahun 2024 sebesar 3,10%, sementara pertumbuhan investasi daerah pada tahun 2024 sebesar 6,79% lebih baik dari Tahun 2023 sebesar -0,56%. Peningkatan pertumbuhan realisasi investasi tersebut belum berdampak besar pada pertumbuhan ekonomi di Kabupaten Indragiri Hilir, hal tersebut terjadi dikarenakan adanya masalah yaitu Ketergantungan pada Sektor Pertanian dan Industri Pengolahan, Penurunan Produksi Sektor Pertanian, Keterbatasan Infrastruktur dan Aksesibilitas. Dengan menggunakan metoda USG (Urgency, Seriousness, Growth), permasalahan yang akan dibahas dalam makalah kebijakan ini yaitu Keterbatasan Infrastruktur dan Aksesibilitas dengan problem statement Keterbatasan infrastruktur dan aksesibilitas di Kabupaten Indragiri Hilir dikarenakan minimnya infrastruktur pendukung investasi yang menyebabkan terbatasnya kawasan industri di Kabupaten Indragiri Hilir sehingga menghambat arus investasi, meningkatkan biaya logistik, dan memperlambat pertumbuhan ekonomi daerah. Langkah yang harus dilakukan oleh Pemerintah Daerah Kabupaten Indragiri Hilir yaitu Pengembangan Kawasan Ekonomi dan Industri, Penguatan Infrastruktur Fisik, Digitalisasi Infrastruktur Ekonomi, Kolaborasi dan Kemitraan, Regulasi dan Kebijakan Pro-Investasi.Langkah tersebut tidak dapat dilaksanakan secara serentak oleh karenanya dibagi dalam 3 program, yaitu program jangka pendek, program jangka menengah, dan program jangka panjang.
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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) | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".