<![CDATA[The culinary industry plays a vital role in driving creative economic growth and local tourism while also being an integral part of urban lifestyle. Given the high number and diversity of culinary locations, clustering techniques are needed to group them based on marketing characteristics, enabling more efficient decision-making for both consumers and businesses. This study aims to cluster culinary locations based on marketing-related attributes using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. Secondary data was obtained from Kaggle, consisting of restaurant information in Semarang City, with attributes such as rating, number of reviews, and operating hours. After preprocessing and exploratory analysis, DBSCAN was applied with adjusted parameters to generate optimal clusters. The results produced 41 clusters with diverse characteristics, including several outliers detected as noise. Performance evaluation using Silhouette Score and Davies-Bouldin Index showed that DBSCAN achieved more compact and well-separated clusters compared to K-Means. These findings demonstrate that DBSCAN is more adaptive for non-uniform culinary data with varying densities and is suitable for segmentation and strategic decision-making in the culinary industry.]]>
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