Perbandingan Metode Cost Sensitive pada Decision Tree dan Naïve Bayes untuk Klasifikasi Data Multiclass

Abstract

Abstrak– Knowledge discovery is the method of extracting information from data in making informed decisions. Seeing as classifiers do have a lot of learning patterns in the data, testing an imbalanced dataset becomes a major classification issue. The cost-sensitive approach on the decision tree C4.5 and nave Bayes is used to solve the rule of misclassification. The glass, lympografi, vehicle, thyroid, and wine datasets were collected from the UCI Repository and included in this analysis. Preprocessing attribute selection with particle swarm optimization was used to process the data collection. Besides, the cost-sensitive decision tree C4.5 and the cost-sensitive naive Bayes method were used in the research. On the glass, lympografi, vehicle, thyroid, and wine datasets, the accuracy of the test results was 72.34 %, 68.22 %, 75.68 %, 93.82 %, and 93.95 %, respectively, using the cost-sensitive decision tree C4.5. While the cost-sensitive naive Bayes method outperforms the others by 32.24 %, 82.61 %, 25.53 %, 97.67 %, and 94.94 % on the dataset, respectively.