Analisis Faktor-Faktor Yang Mempengaruhi Pola Permukiman Sebagian Wilayah Kecamatan Kambu Kota Kendari
Bibliographic record
Abstract
Jumlah penduduk Indonesia dari tahun ke tahun selalu bertambah. Kondisi ini akan membawa konsekuensi semakin bertambahnya kebutuhan ruang hidup yang berupa lahan permukiman. Penelitian ini bertujuan untuk: (1) mengetahui dan memahami pola permukiman penduduk sebagian wilayah Kecamatan Kambu Kota Kendari, dan (2) mengetahui faktor-faktor yang mempengaruhi pola permukiman penduduk sebagian wilayah Kecamatan Kambu Kota Kendari. Metode analisis yang digunakan pada penelitian ini adalah: (1) Nearest Neighbour Analysis dan (2) analisis scoring/pembobotan. Hasil Penelitian ini menunjukan bahwa: (1) sebagian wilayah Kecamatan Kambu terdapat empat kelurahan, yaitu Kelurahan Kambu, memiliki pola mengelompok dan acak di beberapa RT, Kelurahan Lalolara, Padaleu, dan Mokoau memiliki pola mengelompok. Dan memiliki tiga kelas permukiman, yaitu kelas permukiman teratur, semi teratur, dan tidak teratur dari beberapa RT/RW nya; (2) faktor-faktor yang berpengaruh pada pola permukiman, yaitu faktor aksessibilitas, pendapatan atau ekonomi, aspek sosial, dan aspek budaya. Kata kunci: Permukiman, Pola Permukiman, Faktor-Faktor Pola Permukiman, Nearest Neighbour Analysi
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".