Comparison of Support Vector Machine, Random Forest, and C4.5 Algorithms for Customer Loss Prediction

Bima Maulana (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Dany Febrian (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Irgie Rachmat Fachrezi (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Muhammad Ferdi Zeen (International University of Africa Khortum)

Indonesian Journal of Applied Technology and Innovation Science
Vol. 2 No. 1 (2025): IJATIS February 2025

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.