Strategi Peningkatan Daya Tarik Wisata Di Kawasan Agrowisata Pucak Teaching Farm Di Kabupaten Maros
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. This study aims to identify supporting and inhibiting factors in increasing tourism attraction in the Pucak Teaching Farm Agrotourism Area in Maros Regency by using qualitative and quantitative approaches. The analysis method used to determine the supporting and inhibiting factors in increasing tourism attraction is to use quantitative descriptive analysis and to answer strategies to increase tourism attraction using Internal and External Strategic Factors (IFAS – EFAS) analysis. The results of the study show that the supporting factors are weather conditions, good accessibility and supporting facilities, so the strategy that can be used by optimizing supporting factors and fulfilling inhibiting factors to increase tourist attraction again. Abstrak. Penelitian ini bertujuan untuk mengidentifikasi faktor pendukung dan penghambat dalam meningkatkan daya tarik wisata di Kawasan Agrowisata Pucak Teaching Farm di Kabupaten Maros dengan menggunakan pendekatan kualitatif dan kuantitatif. Adapun metode analisis yang digunakan untuk mengetahui faktor pendukung dan penghambat dalam meningkatkan daya tarik wisata yaitu menggunakan analisis deskriptif kuantitaif dan untuk menjawab strategi meningkatkan daya tarik wisata menggunakan analisis Faktor Startegi Internal dan Eskternal (IFAS – EFAS). Hasil penelitian menunjukkan faktor pendukung yaitu kondisi cuaca, aksesibilitas yang baik dan fasilitas penunjang dan pendukung maka strategi yang dapat digunakan dengan melakukan pengoptimalan faktor pendukung dan pemenuhan faktor penghambat untuk meningkatkan kembali daya tarik wisata.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it