<![CDATA[This study analyzes the quality of car body units using data mining classification with the Naive Bayes algorithm. The problem addressed is the high potential for defects in production processes, which requires an effective analytical method to classify body unit quality accurately. The objective of this research is to determine whether the Naive Bayes classification technique can be applied to assess car body unit quality and to evaluate its performance through accuracy, precision, and recall metrics. The research follows standard data mining stages, including data preparation, processing, and classification. Data processing and model testing were conducted using the RapidMiner tool. Several data split scenarios were applied to measure performance improvements based on the amount of data used. The results show that model performance increases as more data is processed. For data proportions of 70%, 80%, and 90%, the accuracy levels achieved were 99.62%, 99.66%, and 99.70%, respectively. Similarly, precision and recall values also improved with larger datasets. Based on these findings, it can be concluded that the Naive Bayes algorithm is effective for classifying car body unit quality and can support quality control processes in automotive manufacturing.]]>
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