Analisis Kesesuaian Lokasi Fasilitas Kesehatan Pariwisata dengan Pendekatan Network dan SMCA–AHP di Kecamatan Kuta Selatan
Bibliographic record
Abstract
Pertumbuhan pesat sektor pariwisata telah meningkatkan kebutuhan akan fasilitas kesehatan yang merata, mudah dijangkau, dan mampu mendukung aktivitas wisata. Penelitian ini bertujuan untuk menganalisis keterjangkauan spasial fasilitas kesehatan serta menentukan lokasi kesesuaian pengembangan baru yang sesuai dengan kondisi fisik, sosial, dan infrastruktur wilayah. Penelitian ini mengombinasikan Network analysis untuk menilai jangkauan pelayanan dan efisiensi akses, serta Spatial Multicriteria Analysis (SMCA) dengan pendekatan Analytical Hierarchy Process (AHP) guna memberikan bobot pada delapan kriteria penentu lokasi. Distribusi fasilitas kesehatan di Kecamatan Kuta Selatan belum merata, dengan jarak rata-rata antara objek wisata dan fasilitas kesehatan sebesar 2.217,89 meter. Desa Pecatu dan Desa Kutuh memiliki aksesibilitas rendah terhadap layanan kesehatan, sedangkan Kelurahan Jimbaran dan Desa Pecatu menjadi wilayah dengan tingkat kesesuaian tertinggi untuk pengembangan fasilitas kesehatan. Integrasi Network analysis dan SMCA–AHP terbukti efektif untuk mendukung keberlanjutan kawasan wisata berbasis kesehatan di Kuta Selatan.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".