The The Potential of Pantai Indah Kemangi for Marine Tourism in Kendal
Bibliographic record
Abstract
Background: Kendal Regency has significant potential in the tourism sector, particularly in marine tourism. One of the iconic beaches is Pantai Indah Kemangi, located in Jungsemi Village, Kangkung District. Pantai Indah Kemangi needs to be developed to maximize its potential as a tourism industry hub. Objective: The purpose of this research is to analyze the potential of Pantai Indah Kemangi as a marine tourism destination in Kendal. This study identifies the factors that support the development of this beach as a potential tourist destination. Method: This research employs a descriptive qualitative research method, with data collected through interviews, secondary data analysis, and field observations. Result: Pantai Indah Kemangi has great potential to develop as a leading marine tourism destination in the North Coast region. This area can stimulate local economic growth, especially with the development of Micro, Small, and Medium Enterprises (MSMEs) in Jungsemi Village. The local government has also undertaken reforestation around the beach by planting sea pines, emphasizing environmental sustainability and the coastal ecosystem. Discussion: Pantai Indah Kemangi has significant potential to become an iconic marine tourism destination in the North Coast (Pantura) area. However, to optimize this potential, the road access needs to be revitalized to accommodate larger vehicles, and adequate lighting should be added. Conclusion: Pantai Indah Kemangi has made a substantial contribution to the development of marine tourism in Kendal, attracting both local and out-of-town tourists. Therefore, to ensure the continued growth of this tourist area, more effective promotion and improved services that do not disadvantage consumers are necessary
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".