PENGEMBANGAN MODEL KLASTERISASI WISATA GASTRONOMI UNTUK MENUNJANG PELESTARIAN KULINER TRADISIONAL - Dewi Hermawati Wahyuningsih; Sulistyowati, Afiefah; Darmaesti, Darmaesti (Jurnal Sains Terapan Pariwisata, 2025)
This study aims to map gastronomic tourism destinations in Surakarta City to support the preservation of traditional culinary delights through the application of the K-means Clustering algorithm. The research data included 21 destinations assessed based on four main variables: culinary, culture, economy, and infrastructure. The analysis process began with data normalization, followed by clustering to obtain optimal grouping based on a Silhouette Score of 0.3264. The results revealed three main clusters: the Very Good Cluster, with high scores on all variables and the potential to become a gastronomic tourism icon; the Fairly Good Cluster, with moderate quality and development opportunities through innovation and capacity building; and the Poor Cluster, with relatively low cultural and economic value despite adequate infrastructure. These findings indicate the need for different development strategies for each cluster, such as large-scale promotion for superior clusters, service quality development for medium clusters, and strengthening traditional values for low clusters. This study demonstrates that clustering can be a strategic tool in formulating policies for the development of traditional culinary delights. The limitation of this research lies in the geographical scope which only covers Surakarta City, so further research is suggested to expand the study area and consider additional variables such as environmental sustainability and the role of social media. Objective: This research aims to develop an innovative gastronomic tourism clustering model to support the preservation of traditional culinary delights. This model is expected to serve as a strategic foundation for mapping and developing integrated and sustainable gastronomic tourism destinations in Indonesia. Research Methods: Using a mixed-methods approach, this study combines quantitative analysis using the K-means Clustering Algorithm and qualitative analysis through in-depth interviews and field observations. Data was collected from 21 gastronomic tourism destinations in Surakarta. The modeling will be implemented in a website-based information system using Codeigniter 4. Implications: This clustering model has significant implications for formulating more targeted policies and programs for the preservation of traditional culinary delights. With systematic clustering, the government and businesses can develop tailored strategies for each cluster, increasing the economic and cultural value of culinary delights, and ensuring their sustainability.