Clustering Culinary Locations Using the DBSCAN Algorithm

Anestin Halawa, Andre Hasudungan Lubis

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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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Referensi


K.-S. Lee, “Culinary aesthetics: World-traveling with culinary arts,†Ann. Tour. Res., vol. 97, p. 103487, 2022.

M. J. Stone, S. Migacz, and E. Sthapit, “Connections between culinary tourism experiences and memory,†J. Hosp. Tour. Res., vol. 46, no. 4, pp. 797–807, 2022.

A. R. Putra, E. Ernawati, J. Jahroni, T. S. Anjanarko, and E. Retnowati, “Creative economy development efforts in culinary business,†J. Soc. Sci. Stud., vol. 2, no. 1, pp. 21–26, 2022.

I. C. Dewi et al., Trend Bisnis Food and Beverages Menuju 2030. Penerbit Lakeisha, 2022.

M. Mutmainna, T. Kumalasari, and Y. Yunarti, “Analysis of the Business Environment and Entrepreneurship Strategy in Improving SME Culinary Enterprises,†J. Econ. Resour., vol. 7, no. 2, pp. 150–159, 2024.

S. Pitafi, T. Anwar, and Z. Sharif, “A taxonomy of machine learning clustering algorithms, challenges, and future realms,†Appl. Sci., vol. 13, no. 6, p. 3529, 2023.

A. H. Lubis and E. Ramayana, “A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data,†Int. J. Recearch Rev., vol. 10, no. 9, pp. 162–169, 2023.

M. S. Rahman, T. D. Sarkar, and M. Y. Emon, “Comprehensive Geospatial and Statistical Analysis of Restaurant Distribution Using K-means Clustering in Machine Learning Technology,†in 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE), 2024, pp. 461–466.

K. Äokić, K. Potnik Galić, and K. Å tavlić, “Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data,†in 10th International Scientific Symposium Region, Entrepreneurship, Development (RED 2021), 2021, pp. 677–690.

A. J. S. Al’Ayubi, A. S. Sunge, and others, “Application of the K-Means Clustering Algorithm for Sales Analysis in a Padang Restaurant Business,†Int. J. Educ. Life Sci., vol. 3, no. 1, pp. 1643–1654, 2025.

E. M. S. Rochman et al., “A combination of algorithm agglomerative hierarchical cluster (AHC) and K-means for clustering tourism in Madura-Indonesia,†J. Math. Comput. Sci., vol. 12, p. Article--ID, 2022.

A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,†Inf. Sci. (Ny)., vol. 622, pp. 178–210, 2023.

A. Fawzia Omer, H. A. Mohammed, M. A. Awadallah, Z. Khan, S. U. Abrar, and M. D. Shah, “Big data mining using K-Means and DBSCAN clustering techniques,†in Big Data Analytics and Computational Intelligence for Cybersecurity, Springer, 2022, pp. 231–246.

H. V. Singh, A. Girdhar, and S. Dahiya, “A Literature survey based on DBSCAN algorithms,†in 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022, pp. 751–758.

A. Amato and V. Di Lecce, “Data preprocessing impact on machine learning algorithm performance,†Open Comput. Sci., vol. 13, no. 1, p. 20220278, 2023.

E. J. M. Carranza, “Exploratory data analysis,†Encycl. Math. Geosci., pp. 1–5, 2021.

A. H. Lubis, W. R. Utami, and J. H. Lubis, “Implementation of k-means clustering for the job provision in urban village,†J. Mat. Dan Ilmu Pengetah. Alam LLDikti Wil. 1, vol. 3, no. 1, pp. 21–31, 2023.




DOI: http://dx.doi.org/10.30811/jaise.v5i3.7512

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