Abstract
Demands for quality assurance of food products are increasing due to the implementation of free trade. These demands require improvements in the application of food quality control process. SMEs in Indonesia inspect the quality of ground coffee manually, the subjectivity and long inspection time causing the SMEs have not been widely applying the consistent quality control system. This study aims to identify the quality control point of ground coffee, classify the quality following Indonesian National Standard (SNI) requirements and determine the corrective or preventive actions needed to produce a consistent, integrated quality control system for ground coffee SME. System requirements are analyzed based on their complexity in real-world using BPMN 2.0. The system made by clustering method with the K-means algorithm. K-Means divides the data into clusters, with data in one cluster sharing similar features but differing from other clusters. The data given is quality data from the bean and ground coffee quality testing. The final results of this study are three clusters of ground coffee quality and association rules determined by the Association Rule Mining method. These rules may use as a reference in taking corrective actions and preventive actions for a nonconformance product.
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