Strategi Pengembangan Ekowisata Mangrove di Desa Karangsong Kabupaten Indramayu
Bibliographic record
Abstract
Abstract. The mangrove forest in the coastal area of Karangsong Village has its own unique attraction because it offers tourist attractions that are different from other tourist attractions, namely mangrove forests and Lestari Beach tourism. Until now, it is still the only mangrove forest ecotourism in Indramayu Regency, which is managed by KSM Pantai Lestari. However, as time goes by, visitors coming to tourist attractions have greatly decreased. Due to the conversion of land into ponds, mangrove logging, poorly maintained mangrove forests, and undeveloped tourist activities. This research aims to formulate a strategy for developing mangrove ecotourism. The approach used is using a mix method or a combination of qualitative and quantitative methods. The types of data used are primary and secondary data obtained from observations, interviews and questionnaires. The results of this research are strategies in ecotourism by carrying out strong marketing efforts, developing facilities, and involving local communities to optimally utilize the potential of mangrove ecotourism. However, it is important to pay attention to the environmental and social impacts of this strategy to ensure the sustainability of mangrove ecotourism. Abstrak. Hutan mangrove di kawasan pesisir Desa Karangsong memiliki keunikan daya tarik tersendiri karena menawarkan atraksi wisata yang berbeda dari objek wisata lainnya yaitu hutan mangrove dan wisata Pantai Lestari dah sampai saat ini masih merupakan satu-satunya ekowisata hutan mangrove yang ada di Kabupaten Indramayu, yang dikelola oleh KSM Pantai Lestari. Namun seiring berjalannya waktu pengunjung yang datang ke objek wisata sangat menurun. Karena adanya alih fungsi lahan menjadi tambak, penebangan mangrove, hutan mangrove yang kurang terawat, dan aktivitas wisata yang belum berkembang. Penelitian ini bertujuan untuk merumuskan strategi pengembangan ekowisata mangrove. Pendekatan yang dipakai yaitu menggunakan mix method atau gabungan dari metode kualitatif dan kuantitatif. Jenis data yang digunakan yaitu data primer dan sekunder yang diperoleh dari hasil observasi, wawancara, dan kuesioner. Hasil dari penelitian ini yaitu strategi dalam ekowisata dengan melakukan upaya pemasaran yang kuat, pengembangan fasilitas, dan keterlibatan masyarakat setempat untuk memanfaatkan potensi ekowisata mangrove secara optimal. Namun, penting untuk memperhatikan dampak lingkungan dan sosial dari strategi ini guna memastikan keberlanjutan ekowisata mangrove
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".