Potensi Kabupaten Simalungun dalam Menerapkan Konsep Smart Tourism melalui Infrastruktur TIK
Bibliographic record
Abstract
The advantages of the tourism sector are currently experiencing expansion and ongoing consideration compared to the manufacturing sector. In the current tourism development, various regions offer advanced and innovative services through the application of the so-called Technology and Communication for tourists who often use Smart Tourism. Currently, the implementation of the Smart Tourism platform is mostly applied in urban tourist areas that have complete basic infrastructure, a good transportation system, the availability of adequate Information and Communication Technology infrastructure, and a comprehensive service system. This surely makes the concept of Smart Tourism is still rarely applied in the Regency area which has great potency and attractiveness. The study is designed to identify the potency of Simalungun Regency in implementing smart tourism in terms of ICT infrastructure. This study uses a qualitative descriptive approach with interview data collection techniques, observation and documentation studies. From the results of the study, it was found that the tourism ICT infrastructure of Simalungun Regency is still not well developed in applying the concept of smart tourism. Simalungun Regency has only used QR codes, social media, and a recommendation system for tourists in the classification of smart tourism technology
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".