Comparison of the Performance of the DBSCAN and ST-DBSCAN Text Mining Algorithms for the Distribution of Ornamental Fish Sales
Keywords:
Clustering, DBSCAN, Ornamental Fish, ST-DBSCAN, Word2VecAbstract
The ornamental fish trade in Indonesia is growing rapidly through e-commerce and social media. The large, diverse, and unstructured sales data complicates accurate market mapping. This study analyzes the distribution of ornamental fish sales using density-based clustering algorithms, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which identifies clusters based on data density, enabling the formation of global distribution patterns while detecting outliers, and ST-DBSCAN (Spatial-Temporal DBSCAN), an extension of DBSCAN, which incorporates spatial dimensions to provide more detailed mapping based on geographic proximity. Data were collected through e-commerce platforms and social media such as Twitter. The results revealed that ornamental fish distribution is primarily concentrated in Java and also distributed in Sumatra, Kalimantan, Bali, and Sulawesi. DBSCAN produced five clusters with two noise points and achieved a Silhouette Coefficient of 0.791. Meanwhile, ST-DBSCAN produced six clusters with three noise points, a Silhouette Coefficient of 0.656, and a Davies-Bouldin Index of 0.985. Overall, DBSCAN effectively represents global distribution patterns and detects anomalies, while ST-DBSCAN enhances the analysis with spatial insights that highlight geographic variations. Together, these two algorithms provide a more comprehensive understanding of the distribution of ornamental fish sales by species and location.
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