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All Journal Indonesian Journal of Applied Technology and Innovation Science
Muhammad Ferdi Zeen
International University of Africa Khortum
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparison of Support Vector Machine, Random Forest, and C4.5 Algorithms for Customer Loss Prediction Bima Maulana; Dany Febrian; Irgie Rachmat Fachrezi; Muhammad Ferdi Zeen
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 1 (2025): IJATIS February 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i1.1102

Abstract

Loss of customers has been discussed and many studies have been conducted, starting from using the Bayesian network algorithm, Decision tree, random vorest, Support vector machine, and neyral network Algorithms Support Vector Machine (SVM), Random Forest, and Decision Tree or C4.5 are algorithms used for prediction and have several advantages Random forest has the advantage of being able to combine many predictions from decision trees that have a tendency to reduce overfitting. This research uses the C4.5 algorithm, SVM and random forest. Research shows that the Random Forest method has the highest accuracy of 87.02% compared to the Support Vector Machine and Decision Tree methods. In contrast, Decision Tree gets low accuracy results with a value of 78.52%. Experimental results show that the Random forest method for customer loss prediction achieves an average classification accuracy of 4% - 9% higher than the Support Vector Machine and Decision Tree methods.
Co-Authors Bima Maulana Dany Febrian Irgie Rachmat Fachrezi