PEMANFAATAN SISTEM INFORMASI GEOGRAFIS UNTUK ANALISIS KUALITAS LINGKUNGAN PERMUKIMAN DI KECAMATAN LABUHAN RATU KOTA BANDAR LAMPUNG TAHUN 2025
Bibliographic record
Abstract
Kecamatan Labuhan Ratu merupakan wilayah yang ditetapkan sebagai pusat pendidikan tinggi di Kota Bandar Lampung. Laju pertumbuhan pendudukdan kepadatan bangunan yang tinggi membuat lahan terbangun semakin meningkat, dan dapat menjadi beban bagi kualitas lingkungan, serta berpengaruhterhadap kenyamanan serta kesehatan masyarakat. Penerapan teknologi penginderaan jauh dan sistem informasi geografis (SIG) dapat dimanfaatkandalam penilaian kualitas lingkungan permukiman. Tujuan penelitian ini adalah menganalisis kondisi kualitas lingkungan permukiman di Kecamatan Labuhan Ratu Kota Bandar Lampung. Metode yang digunakan adalah metode analisiskuantitatif berjenjang tertimbang melalui pengharkatan dan pembobotan padaparameter kepadatan bangunan permukiman, tata letak bangunan, lebar jalanmasuk permukiman, pohon pelindung, dan lokasi permukiman. Kualitas permukiman di Kecamatan Labuhan Ratu terbagi menjadi 3 kelas kualitas, yaitu kualitas baik dengan nilai skor 24–30, kualitas sedang dengan nilai skor 17–23,dan kualitas buruk dengan nilai skor 10–16. Kecamatan Labuhan Ratu didominasioleh blok permukiman dengan kualitas sedang sebanyak 70 blok dari total 123blok atau sebesar 56,64% dari total luas seluruh blok permukiman. Kata kunci: kualitas lingkungan, permukiman, penginderaan jauh, sistem informasi geografis
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".