Analisis Clustering Data Produk Menggunakan Algoritma Self Organizing Map
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Abstract
Product data with diverse characteristics require effective grouping methods to identify patterns and data structures. Clustering techniques in data mining can be applied to address this problem. This study aims to analyze the clustering of product data using the Self Organizing Map (SOM) algorithm. The dataset used is the Product Classification and Clustering dataset obtained from the UCI Machine Learning Repository. The research method includes attribute selection, data normalization, SOM model training, and clustering evaluation. All data processing and model training are performed using RapidMiner software. The results show that the SOM algorithm successfully forms five product data clusters with different characteristics. The data distribution in each cluster ranges from approximately 15% to 25% of the total dataset, indicating a relatively balanced clustering result. Clustering performance evaluation using the Davies–Bouldin Index yields a value of −0.723, which indicates good cluster separation. Based on these results, it can be concluded that the Self Organizing Map algorithm is effective for clustering product data and provides a well-structured representation of data patterns.
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