<![CDATA[Advances in information technology have encouraged the use of data mining to support decision-making, particularly in the government sector. Soko District faces challenges in objectively assessing employee needs because the assessment is still carried out manually, resulting in uneven employee distribution. This study aims to classify the level of employee sufficiency using the Naïve Bayes method with a quantitative approach supported by observation, interview, and documentation data. The analyzed data consisted of 7 datasets representing each section in Soko District, analyzed based on the attributes of employee number and workload. Testing was carried out through manual calculations and validated using RapidMiner. The results showed that most of the data were categorized as Insufficient with a posterior probability value higher than the Sufficient category. Testing using RapidMiner produced an accuracy rate of 50%, indicating that the model was able to classify some of the data but still faced challenges in identifying optimal patterns. The Naïve Bayes method can be used to categorize the level of employee sufficiency and provide an initial overview to support more objective and data-driven decision-making.]]>
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