PERENCANAAN INFRASTRUKTUR BERKELANJUTAN DI KABUPATEN MAHAKAM ULU: PEMETAAN AKSESIBILITAS DAN POTENSI PENGEMBANGAN WILAYAH SEBAGAI DAERAH PENYANGGA IKN
Bibliographic record
Abstract
Kabupaten Mahakam Ulu, yang strategis di Kalimantan Timur, menghadapi tantangan besar dalam konektivitas dan distribusi infrastruktur yang mempengaruhi pertumbuhan sosial-ekonomi. Penelitian ini bertujuan untuk menganalisis aksesibilitas dan infrastruktur, dengan fokus pada disparitas akses terhadap fasilitas dasar dan potensi pengembangan wilayah sebagai daerah penyangga Ibu Kota Nusantara (IKN). Menggunakan teknologi GIS, seperti Diagram Voronoi dan Kernel Density Estimation (KDE), penelitian ini memetakan distribusi infrastruktur dan aksesibilitas antar kecamatan. Hasil analisis menunjukkan ketimpangan signifikan, terutama di kecamatan seperti Long Apari dan Long Pahangai. Rekomendasi mencakup peningkatan jalan lokal dan kolektor, penguatan transportasi sungai, serta pengembangan fasilitas dermaga dan jalan darat terintegrasi untuk mendukung sektor pariwisata, pertanian, dan industri lokal. Peningkatan sistem drainase dan pemeliharaan sungai juga diperlukan untuk mengurangi risiko banjir. Dengan teknologi GIS, perencanaan pembangunan yang efisien dan merata dapat terwujud, mendukung keberlanjutan Kabupaten Mahakam Ulu sebagai daerah penyangga IKN
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".