<![CDATA[Direct Cash Assistance is a social program distributed to residents meeting specific requirements. The village government determines the recipients using a conventional system through village meetings. This approach is greatly influenced by the decision-holders’ subjectivity with non-transparent thinking. This research aims to solve the problem of classifying Direct Cash Assistance recipients by applying probability-based classification. The research method used is smoothed Nave Bayes, which improves Nave Bayes by adding a constant to avoid zero classification. The datasets use variables such as age, type of work, and criteria for receiving assistance. The last variable includes five nominal data, which debilitates Nave Bayes by not obtaining a posterior probability as a prediction class result. We used Direct Cash Assistance data from the Sedati sub-district, Sidoarjo district, East Java. The results of research with original Nave Bayes and smoothed Nave Bayes classification show that smoothed Nave Bayes has good prediction performance with an accuracy of 95.9% with a data split of 60:40. Smoothed- Nave Bayes also solves the problem of 8 data without predictive classes. The prediction results show that Smoothed Nave Bayes performs better than standard Nave Bayes. This research contributes to refining Nave Bayes to complement probability-based classification by adding refinement constants to avoid zero classification.]]>
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