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Perencanaan Dan Pengembangan Homestay Di Desa Wisata Angsana, Desa Setu, Kab. Bogor

2023· article· id· W4388244362 on OpenAlexaff
Matthew Arifin, Christopher Deo, Budi Setiawan

Bibliographic record

VenueIKRA-ITH ABDIMAS · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Desa Wisata Angsana merupakan satu dari banyaknya desa wisata yang terdapat di Desa Setu, Kecamatan Jasinga, Kabupaten Bogor. Dengan adanya desa wisata ini, dimimpikan dapat memberikan kesempatan dan peluang kepada masyarakat sekitar untuk memanfaatkan potensi dan sumber daya yang terdapat di desa wisata ini guna untuk mendukung keberlangsungan dan pertumbuhan pariwisata di Desa Wisata Angsana. Pengembangan desa wisata ini dilihat dari empat komponen pariwisata yang sudah ada di desa wisata ini, yaitu Attraction, Accessibilities, Amenities, Acomodation. Komponen pariwisata ini berguna untuk membantu mengindentifikasi masalah dan solusi yang akan digunakan dalam pengembangan wisata desa. Metode pengabdian masyarakat yang digunakan yaitu metode deskriptif kualitatif, dengan teknik pengumpulan data melalui observasi, wawancara, dan studi dokumentasi. Rencana pengembangan di desa wisata ini adalah membenahi homestay dan pembuatan tour package. Pembenahan yang dilakukan terhadap homestay di Desa Wisata Angsana bertujuan untuk mengembangkan Desa Wisata Angsana dari segi Amenitas dan menciptakan daya tarik. Pembenahan ini juga ditujukan untuk menonjolkan keunggulan dari homestay itu sendiri. Selain itu, dari sudut pandang mahasiswa, pembenahan homestay juga dapat meningkatkan empati dan kepedulian mahasiswa terhadap keberadaan Desa Wisata Angsana yang sangat berpotensi untuk menjadi desa wisata secara penuh. Selain pembenahan homestay, dilakukan juga pembuatan Tour Package yang bertujuan sebagai media promosi Desa Wisata Angsana. Angsana Tourism Village is one of many tourist villages located in Setu Village, Jasinga District, Bogor Regency. With this tourism village, it is envisioned to be able to provide opportunities and opportunities for the surrounding community to take advantage of the potential and resources contained in this tourist village in order to support the sustainability and growth of tourism in Angsana Tourism Village. The development of this tourist village is seen from the four tourism components that already exist in this tourist village, namely Attraction, Accessibilities, Amenities, Accommodation. This tourism component is useful to help identify problems and solutions that will be used in the development of village tourism. The community service method used is a qualitative descriptive method, with data collection techniques through observation, interviews, and documentation studies. The development plan for this tourist village is to improve homestays and make tour packages. The improvements made to the homestay in Angsana Tourism Village aim to develop Angsana Tourism Village in terms of amenities and create attractiveness. This improvement is also intended to highlight the advantages of the homestay itself. In addition, from a student's point of view, revamping homestays can also increase student empathy and concern for the existence of Angsana Tourism Village which has the potential to become a full-fledged tourist village. In addition to revamping the homestay, a Tour Package was also made which aims to be a promotional medium for Angsana Tourism Village.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0430.009

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 head0.032
GPT teacher head0.301
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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