KOMPARASI ALGORITMA NAIVE BAYES, RANDOM FOREST DAN SVM UNTUK MEMPREDIKSI NIAT PEMBELANJA ONLINE

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  • 02 Mar
  • 2022

KOMPARASI ALGORITMA NAIVE BAYES, RANDOM FOREST DAN SVM UNTUK MEMPREDIKSI NIAT PEMBELANJA ONLINE

In recent years, the use of e-commerce or online shops has increased considerably. Various online stores have sprung up on the internet, both small and large-scale. This has a very important effect on the effective use of time and the level of sales figures. Therefore, e-commerce or online stores must have the ability to assess the means used to identify and classify online shopping intentions to generate profits for the store. Classification of online shopper intentions can be carried out using several algorithms, such as Naive Bayes, Random Forest, and Support Vector Machine. In this study, the comparison of algorithms was carried out using the WEKA application by knowing the value of the F1-Score, Accuracy, Kappa Statistic, and Mean Absolute Error. There is a difference between the test results, for the F1-Score, Accuracy, Kappa Statistic results in the Random Forest algorithm test which is the best compared to Naive Bayes and Support Vector Machine. While the Mean Absolute Error test results for the Support Vector Machine algorithm are the best values than Naive Bayes and Random Forest. So based on this research the Random Forest Algorithm is the best and appropriate algorithm to be applied as a classification of online shopper intentions because the Random Forest algorithm dominates in knowing the value of criteria such as F1-Score, Accuracy, Kappa Statistic, and Mean Absolute Error

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