<![CDATA[Oil palm is one of the important plantation commodities in Indonesia, so seed quality is a major factor in production success. The main problem in the field is that seed quality determination is still done manually, which takes a long time and is prone to human error. Therefore, this study aims to minimize human error and support decision-making in determining planting priorities for superior seeds through the classification of oil palm seed quality using the Naïve Bayes algorithm. The model was built based on three main parameters, namely moisture content, storage room humidity, and seed storage duration. The results were labeled as low, medium, and high quality categories. Testing results using an 80% of data training (130 data) and 20% of data testing (32 data) model splitting, that the Naïve Bayes model produced an accuracy of 91% from 162 dataset. The classification results showed that 38 data points fell into the low quality category, 55 into the medium category, and 56 into the high category. The research results should be more oriented towards statements regarding the ability of Naïve Bayes to classify palm oil seed types, so that it can be used as a model recommendation in palm oil determination.]]>
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