ANALISIS KETERSEDIAAN TANAH DI KAWASAN PARIWISATA LIKUPANG
Bibliographic record
Abstract
This study aims to analyze the location of land availability and the direction of land availability from the aspect of Spatial Designation Plans and land issues. The research was carried out from November 2021 to January 2022 which is located in the Likupang tourism area. The method used in this research is Overlay Analysis with GIS software and descriptive analysis obtained through Indepth Interview. The data used are primary data and secondary data. The results of the research analysis obtained that the availability of land at the location was classified into available and unavailable. The area of available land is 829.47 Ha (17.97%) while the area of land that is not available is 3,716.55 Ha (80.54%). The available land locations consist of available non-agricultural cultivation covering an area of 664.86 Ha (14.41%) and agricultural cultivation covering an area of 164.61 Ha (3.57%). The location of land that is not available is limited to protected activities covering an area of 1,030.06 Ha (22.32%), optimal use of non-agricultural land of 217.33 Ha (4.71 %), optimal use of agricultural land covering an area of 2,145.78 Ha ( 46.5%) and land use adjustment of 323.38 Ha (7.01%). The direction of land availability, namely the location of the available land is directed based on the allotment of space so that it is obtained for investment activities covering an area of 664.86 hectares and commodity activities covering an area of 164.61 hectares. At the location of the available land, there is a potential land dispute of 2 hectares. So that the availability of clean and clear land (no dispute) is (827.47%).
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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 | 0.002 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".