Smart Tourist: Karakteristik pada Objek Destinasi Wisata Gunung Karang Kabupaten Pandeglang
Bibliographic record
Abstract
Abstract. Pandeglang Regency is known as an area that has rich tourism destinations, including natural tourism, religious tourism, and water parks. The tourism potential in this area mostly depends on the beauty of natural resources, so it is important for tourists and local communities to maintain its natural conservation. At Gunung Karang Tourism, tourists can enjoy various tourist attractions, including Kampung Domba in Juhut Village and Landmark Saung Biru in Kaduengang Village. Along with the increasing number of tourist visits, the purpose of this study is to determine the characteristics possessed and shown by tourists who come to visit Gunung Karang Tourism in order to support sustainable tourism in Pandeglang Regency. This study uses a mix method approach, with data collection through questionnaires, interviews, and observations. The analysis method used is descriptive statistics based on the results of the questionnaire scoring. The results of the analysis show that tourists on average have a score of 2 and a score of 3, meaning that the characteristics they have already show that they are smart tourists. Abstrak. Kabupaten Pandeglang dikenal sebagai daerah yang memiliki destinasi pariwisata yang kaya potensi, mencakup wisata alam, religi, dan taman bermain air. Potensi pariwisata di wilayah ini mayoritas bergantung pada keindahan sumber daya alam, sehingga penting bagi wisatawan dan masyarakat setempat untuk menjaga konservasi alamnya. Di Wisata Gunung Karang, para wisatawan dapat menikmati berbagai objek wisata, termasuk Kampung Domba di Desa Juhut dan Landmark Saung Biru di Desa Kaduengang. Seiring dengan meningkatnya jumlah kunjungan wisatawan, penting diteliti mengenai karakteristik wisatawan dalam pengembangan pariwisata. Penelitian ini bertujuan untuk mengidentifikasi karakteristik wisatawan pada Objek Destinasi Wisata Gunung Karang dalam rangka untuk mendukung pariwisata berkelanjutan di Kabupaten Pandeglang. Penelitian ini menggunakan metode pendekatan mix method, dengan pengumpulan data melalui kuesioner, wawancara, dan observasi. Metode analisis yang digunakan ialah statistik deskriptif berdasarkan hasil skoring kuesioner. Hasil analisis menunjukkan bahwa wisatawan rata – rata memiliki skor 2 dan skor 3, artinya karakteristik yang dimiliki sudah menunjukkan sebagai seorang smart tourist.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".