Investigating the spatial basis clustering of smart tourism potential using fuzzy c-means
Bibliographic record
Abstract
The expansion of tourism locations that are both creative and of high quality is a significant contributor to the expansion of the economy. The stages of tourism development that are influenced by the progression of information technology are represented by the term "smart tourism" in the context of the ecosystem of smart villages. Integrating micro-enterprises with tourist practices is one of the ways that may be utilised to speed up the development of villages. By implementing the concept of smart tourism, tourism integrated with information and communication technology (ICT) can potentially improve both the economics and the services provided by the tourism industry. This research aims to analyse the clustering of smart tourism potential possibilities within the Kemang sub-district. These areas' clustering depends on some variables, including infrastructure (access for tourists), innovation, technology, local wisdom, distinctiveness, and economic conditions. The Fuzzy C-Means (FCM) clustering approach is utilised. A Geographic Information System (GIS) is utilised to facilitate the process of determining which villages are included in each cluster. This is done to describe potential areas better. The value of the cluster evaluation using the Davies Boulding Index (DBI) obtained is 0.3819, and the number of clusters with the best performance is 3. There is a very potential cluster in Cluster 3, comprising two villages (Kemang and Atangsanjaya). A potential cluster was also detected in Cluster 2, comprising three villages (Tegal, Pondokudik, and Parakanjaya). Furthermore, a fairly potential cluster was detected in Cluster 1, consisting of three villages (Jampang, Pabuaran, and Bojong). Specifically, in the Kemang sub-district, it is anticipated that the findings of this study will provide an overview of possible sites for implementing environmentally conscious tourism.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".