<![CDATA[This study aims to analyze the distribution patterns of sachet machine product weights using the K-Means algorithm as a clustering technique. The dataset consists of 940 entries of primary production records, each containing ten weight measurement samples per production cycle. The data underwent a cleaning process to ensure the absence of missing values, duplicates, and outliers, followed by the selection of relevant attributes (product weight samples) and transformation using Min-Max normalization to scale all variables within the 0–1 range. The clustering process was performed iteratively by updating the centroids until convergence was achieved. The evaluation results indicate that the optimal number of clusters is three (k=3) with a Silhouette Coefficient of 0.55, reflecting a good balance between intra-cluster homogeneity and inter-cluster separation. Cluster 1 represents products with relatively low weights (8.00–8.18 grams), Cluster 2 includes medium-weight products (8.19–8.34 grams), and Cluster 3 consists of high-weight products (8.36–8.98 grams). Overall, the product weights tend to be stable with low variation, although some anomalies were observed in certain machines. These findings demonstrate that the K-Means algorithm can effectively classify product weight data, providing valuable insights for quality control, product variation identification, and minimizing risks of deviation from production standards.]]>
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