(Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, 60111, Indonesia)
(2) Harison Harison
(Universitas Riau, Pekanbaru, Indonesia, 28131, Indonesia)*Corresponding author
AbstractThis study discusses about the classification analysis of the status of home ownership credit at one of the banks in Pekanbaru City using the naïve Bayes algorithm. The purpose of this study is to compare the results of naïve Bayes classification with variable selection using forward and backward selection without variable selection. Variables from the selection results that are included in the best model with transform data use Weight of Evidence method are the type of customer, residential status, last education, and occupation. The results show that the naïve bayes algorithm with variable selection is better than without variable selection. This is shown from the value of the confusion matrix, namely accuracy and specificity, which reached 57,10% and 72,32%, although the sensitivity value was relatively small, 33,10%. KeywordsNaïve bayes algorithm; forward selection; backward selection; weight of evidence; confusion matrix
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DOIhttps://doi.org/10.33122/ejeset.v6i1.684 |
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