<![CDATA[Producing precision components like outer gear couplings requires high geometric and surface quality to maintain mechanical transmission performance in machinery. However, CV Adi Makmur Metalindo experiences recurring quality issues with these specific components. Between July and October 2025, the overall defect rate reached 16.94% (62 defective units out of 366 total units). These issues are primarily geometric defects (62.90%) and surface defects (37.10%). This comprehensive study evaluates production quality performance, identifies dominant process attributes triggering defects, and develops a robust decision support model for quality control. The methodology utilizes a quantitative case study approach, integrating the Six Sigma DMAIC framework with the J48 decision tree algorithm to reinforce the Analyze stage. Results indicate a Defect Per Million Opportunities (DPMO) value of 169,399 and a Defect Per Unit (DPU) of 0.1694, confirming an unstable production process with a high probability of defects. The J48 algorithm modeling clearly identified rough turning RPM (rpm_bubut_kasar) as the dominant predictor classifying product quality. Extracted decision rules reveal that setting machine RPM too low (under 53.9 RPM) or too high (over 65.5 RPM) heavily correlates with geometric defects, whereas the intermediate range triggers surface defects. Consequently, the Improve phase proposes standardizing operational parameters within an optimal range of 55 to 65 RPM. For the Control phase, a mockup of a smart monitoring dashboard interface was designed to efficiently support targeted quality control decisions. Ultimately, integrating Six Sigma DMAIC and the J48 algorithm successfully transforms manufacturing process data into practical decision rules, effectively supporting product quality optimization.]]>
Copyrights © 2026
