<![CDATA[Hope family program is a government program that aims to relieve the burden of poor or near poor families in terms of food, in its implementation is still not optimal because there are still many hopeful family programs that are not yet on target, to avoid any subjective determination of the data mining concept will facilitate overcome problems that have not been optimal, the classification method is able to find the purpose of selecting aid acceptance. The application of genitka can predict the opportunity to visit to determine the difference in results. In this study took 500 data lati data. The lassification methods used in this study are Genetic Algorithms and Naive Bayes. The results of the layoff test for the Naïve Bayes method in accuracy were 94.80% and the genetic algorithm results 69.00% then the ratio dropped by 25.80%. The results of the layoff test for the Naïve Bayes method in the percentage were 94.48% and the genetic algorithm results were 0.00% then the comparison remained unchanged. The results of layoff test for the Naïve Bayes method in recall were 88.38% and the genetic algorithm results were 0.00% then the comparison remained unchanged. Thus the classification using the naïve bayes classification method can be used for selecting aid programs for underprivileged families. Keywords : Naïve Bayes, Algoritma Genetic, RapidMiner, PKH]]>
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