<![CDATA[The sales of electronic products have increased rapidly over the past few years. However, grouping products based on certain criteria is still an unresolved issue. Therefore, research is needed to develop more accurate clustering methods. Currently, the problem with electronic product clustering using the k-means method still has limitations, such as sensitivity to initial centroid values and inability to handle overlap between clusters. Therefore, research is needed to optimize the grid parameter of the Fuzzy C-Means algorithm to produce more accurate clustering. The purpose of this study is to implement the Fuzzy C-Means algorithm with optimized grid parameters to cluster electronic product sales more accurately. The method used in this study is an experimental research method. Electronic product sales data were obtained from specific stores, and the Fuzzy C-Means algorithm with optimized grid parameters was applied to cluster electronic products. The results show that implementing the Fuzzy C-Means algorithm with optimized grid parameters can produce more accurate electronic product clustering compared to the k-means method. By using optimized grid parameters, the Fuzzy C-Means algorithm can handle overlap between clusters and produce more stable centroids with a Dbi accuracy value of 0.510 for Numerical Measure and 0.611 for Mixed Measure.]]>
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