Implementation of Data Mining with Naive Bayes Algorithm for Eligibility Classification of Basic Food Aid Recipients
DOI:
https://doi.org/10.34306/ijcitsm.v2i2.114Keywords:
Naive Bayes, Confusion Matrix, Data Mining, Algorithm.Abstract
One of the primary issues that the government of a nation concentrates on is poverty. The provision of precise and focused data on poverty is a crucial component of the Poverty Reduction Strategy. One technique for classifying data is Naïve Bayes. The aid manager will subsequently use the categorization findings to inform judgments about categorizing and determining who should get basic food assistance. Predictions for those who get basic food assistance fall into two categories: eligible and ineligible. Sample data from the hamlet of XYZ used as the basis for the forecast. In this study, a web-based application is used to construct and assess the Naïve Bayes method. The accuracy for 135 training data, 40 test data, and seven characteristics employed generates 86 percent accuracy, 85 percent recall, and 88 percent precision according to the assessment findings using the confusion matrix.
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Copyright (c) 2022 Yamato Shino, Yusuf Durachman, Nana Sutisna.
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