Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes
 (*)Mulkan Azhari  (Universitas Potensi Utama, Medan, Indonesia)
Zakaria Situmorang (Universitas Katolik Santo Thomas Medan, Medan, Indonesia)
Rika Rosnelly (Universitas Potensi Utama, Medan, Indonesia)
(*) Corresponding Author Submitted: March 19, 2021; Published: April 25, 2021 |
Abstract
In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm
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