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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".