STRATEGI PEMBANGUNAN DAN PENGEMBANGAN PERUMAHAN DAN KAWASAN PERMUKIMAN PROVINSI BANTEN
Bibliographic record
Abstract
Perumahan dan permukiman merupakan salah satu kebutuhan dasarmanusia dalam rangka peningkatan dan pemerataan kesejahteraan rakyat.Penelitian ini bertujuan untuk mengetahui strategi pembangunan danpengembangan perumahan dan kawasan permukiman Kota Serang, ProvinsiBanten. Penelitian yang digunakan melalui pendekatan studi kasus (casestudy approach). Rancangan penelitian yaitu penelitian eksplanatori.Penelitian ini dilakukan pada bulan Agustus sampai Desember2017 diProvinsi Banten. Data yang dikumpulkan dalam penelitian ini terdiri atas dataprimer dan dan sekunder dengan metode pengumpulan data melaluiobservasi, wawancara terstruktur, FGD dan metode pustaka. Analisisproyeksi penduduk mengunakan metode pertumbuhan pendudukeksponensial. Hasil penelitian dianalisis secara analisis Geospasial dandeskriptif. Strategi pengembangan PKP di Provinsi Banten saat ini sangatditentukan oleh kebutuhan hunian masyarakat baik untuk kebutuhan pribadimaupun untuk kebutuhan investasi. Arah pengembangan PKP di ProvinsiBanten pada tahun 2030 sesuai RTRW, maka beberapa kota harus mulaimengembangkan permukiman di wilayah sekitarnya dan ataumengembangkanpermukiman dengan konsep vertikal dan berbasis TODseperti Kota Tangerang Selatan dan Kota Tangerang.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".