Classification of Citizens with Low Economic Level Using Naive Bayes Classification Method
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Abstract
The effort to reduce poverty, it is hoped that the economic growth of a region can be spread evenly. To equalize the economic level of its citizens, local governments provide many opportunities for their citizens to get help or subsidies and even help open businesses so that the welfare of their citizens can be improved. The availability of accurate and sustainable citizens' economic status data is one of the important instruments for evaluating government policies in alleviating poverty by distributing targeted aid. Therefore, this study will conduct a classification based on data from citizens with low economic levels obtained from Pasar Babakan Sub-district, Bogor. The data used in this study is the data of underprivileged residents who are in Pasar Babakan Sub-district Bogor using data mining techniques. The training data are taken from 214 citizens that has a category of underprivileged citizens with attributes used in the classification of income per day, occupation and number of dependents. The test results using the Naive Bayes Classifier method produce 100% accuracy including the excellent category with 100% precision and 100% recall.
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