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Kuera Kusuma
Universitas Buddhi Dharma
Computer Science & IT

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Comparison of C4.5 Algorithm, Naive Bayes and Support Vector Machine (SVM) in Predicting Customers that Potentially Open Deposits Yusuf Kurnia; Kuera Kusuma
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.335 KB) | DOI: 10.32877/bt.v1i2.46

Abstract

This research is based on the application of data mining processing to produce information that is useful in helping decision making. In this study aims to determine the superior algorithm between C4.5, Naive Bayes and SVM algorithms in predicting which customers who have high potential to open deposits. The data used in this study is secondary data where its data is obtained from the UCI dataset. The comparison results of the accuracy value of C4.5 Algorithm 90.57%, accuracy of Naive Bayes 87.70% and SVM 89.29%. Based on the results of the comparison of accuracy values, it is found that the C4.5 algorithm has the highest level of accuracy. So that the application of supporting applications to predict customers who have the potential to open deposits uses the rules for establishing C4.5 data processing.
Co-Authors Yusuf Kurnia