MétaCan
Menu
Back to cohort
Record W4386541839 · doi:10.29313/bcsurp.v3i2.8331

Identifikasi Kesesuaian Atraksi Wisata Pantai Berdasarkan Daya Tampung dan Daya Dukung

2023· article· en· W4386541839 on OpenAlexaff
Widya Maharani, Yulia Asyiawati

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsTourismRecreationCarrying capacityGeographyEnvironmental resource managementEnvironmental sciencePolitical scienceEcologyArchaeology

Abstract

fetched live from OpenAlex

Abstract. Pangandaran Beach has various kinds of tourist attractions such as beach recreation areas, watersport, swimming, snorkeling, mangrove tourism and camping, but these tourist attractions have not considered the suitability of tourist attractions considering that the Pangandaran beach area is a disaster-prone area, for this reason it is necessary to carry out this study to consider the carrying capacity and carrying capacity of tourist attractions. This study aims to identify the types of tourist attractions in the Pangandaran beach area, identify the suitability of tourist attractions and identify the carrying capacity and carrying capacity of beach tourism attractions in the Pangandaran beach area. The analytical method used is the tourism suitability index, carrying capacity analysis by calculating physical carrying capacity (PCC), real carrying capacity (RCC), and effective carrying capacity (ECC) and regional carrying capacity analysis. Based on the results of the suitability index analysis for beach recreation tourism in the appropriate category (84.52%), mangrove ecotourism (64.10%) and snorkeling tourism (56.14%) in the conditional category, based on the results of calculating capacity, the value of PCC (319,051) > RCC (11,928) > ECC (10,504) shows that the tourism capacity of Pangandaran Beach has not been exceeded. The results of the calculation of the area's carrying capacity can accommodate 65,049 people from all types of tourist attractions. From the results of this analysis, what can be recommended from this study is to develop tourist attractions by adding and updating variations in various kinds of tourist attraction activities.
 Abstrak. Pantai Pangandaran memiliki berbagai macam atraksi wisata seperti area rekreasi pantai, watersport, berenang, snorkeling ,wisata mangrove dan berkemah akan tetapi atraksi wisata tersebut belum mempertimbangkan kesesuaian atraksi wisata mengingat kawasan pantai pangandaran merupakan kawasan rawan bencana, untuk itu perlu dilakukan kajian ini untuk mempertimbangkan daya tampung dan daya dukung untuk atraksi wisata. Penelitian ini bertujuan mengidentifikasi jenis atraksi wisata yang terdapat di kawasan pantai pangandaran, mengidentifikasi kesesuaian atraksi wisata dan mengidentifikasi daya tampung dan daya dukung atraksi wisata pantai di kawasan pantai Pangandaran. Metode analisis yang digunakan adalah indeks kesesuaian wisata, analisis daya tampung dengan menghitung daya tampung fisik (PCC), daya tampung riil (RCC), dan daya tampung efektif (ECC) dan analisis daya dukung kawasan. Berdasarkan hasil analisis indeks kesesuaian wisata rekreasi pantai kategori sesuai (84,52%), ekowisata mangrove (64,10%) dan wisata snorkeling (56,14%) kategori sesuai bersyarat, berdasarkan hasil pehitungan daya tampung didapatkan nilai PCC (319.051) > RCC (11.928) > ECC (10.504), menunjukan daya tampung wisata Pantai Pangandaran belum terlampaui. Hasil perhitungan daya dukung kawasan dapat menampung 65.049 orang dari seluruh jenis atraksi wisata. Dari hasil analisis tersebut yang dapat direkomendasikan dari kajian ini adalah melakukan pengembangan atraksi wisata dengan penambahan dan pembaharuan variasi dalam berbagai macam aktivitas atraksi wisata.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.331
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBandung Conference Series Urban & Regional PlanningSame topicCommunity-based Tourism Development and SustainabilityFrench-language works237,207