Pemetaan Potensi Objek Wisata Di Kecamatan Girisubo Kabupaten Gunungkidul Dengan Sistem Informasi Geografis
Bibliographic record
Abstract
ABSTRACT \nGirisubo sub-district is a sub-district directly adjacent to the south coast of Java which holds a lot of tourism potential, but the high potential is still a lot of attractions that have not been developed which amounted to 26 attractions compared to the number of attractions that have been developed, so a study was made entitled "Mapping the Potential of Tourism Objects in Girisubo District, Gunungkidul Regency with the Help of Geographic Information Systems", with the aim of 1) mapping the distribution of potential tourist attractions in Girisubo District, 2) analyzing the level of potential of marine and natural attractions in Girisubo District, and 3) analyzing the factors causing the unmanaged tourist attractions in Girisubo District. The methods used in this research are secondary data analysis method and survey method supported by field observation. Field observations were conducted to support secondary data and aimed to determine the physical condition of the tourist attraction and obtain the coordinate location. While the data analysis method uses scoring and classification analysis methods. The results showed that: 1) tourism objects that have not been developed in Girisubo sub-district are scattered in 4 villages namely Jepitu, Balong, Pucung, and Songbanyu villages, 2) the potential level of tourism objects in Girisubo sub-district is divided into low and medium level classifications, The tourist attractions with low-level classification include Watubonang Beach, Ngusalan Beach, Nglegundi Beach, Brumbun Beach, Mount Minjung, Jepitu Community Forest, Pulejajar Cave River, Mbubuk Beach, Embung Bandung, Embung Dungbendo, Embung Mbendo, Embung Jirak, Embung Tambur, Margatindak Cave, and Manggir Cave. While tourist attractions with a moderate level classification are Pesewan Beach, Dander Beach, Wedanan Beach, Grendan Beach, Ngrengisan Beach, Botorubuh Beach, Watukebo Beach, Sinden Beach, Ngungap Beach, Tanjungmenyer Beach, and Watubolong Beach. 3) As for the factors that have not managed the tourist attraction in Girisubo Sub-district are caused by inadequate human resources, lack of pokdarwis training, cost constraints, minimal accessibility, lack of accommodation, attractions and cooperation with various parties.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".