Penggunaan Algoritma Gaussian Naïve Bayes & Decision Tree Untuk Klasifikasi Tingkat Kemenangan Pada Game Mobile Legends

Yoga Naufal Ray  Putro; Aidil  Afriansyah; Radhinka Bagaskara

doi:10.53842/juki.v6i1.472

Authors

Yoga Naufal Ray Putro Institut Teknologi Sumatera
Aidil Afriansyah Institut Teknologi Sumatera
Radhinka Bagaskara Institut Teknologi Sumatera

DOI:

https://doi.org/10.53842/juki.v6i1.472

Keywords:

Mobile Legends, Classification, Gaussian Naïve Bayes, Decision Tree, Draft Pick

Abstract

The development of technology and the internet has increased the popularity of online games, such as Mobile Legends. However, in competitions, players often experience defeat due to various factors, including player skills, team strategies, and the right hero selection. The right hero selection is very important to increase the chances of winning. Therefore, the Mobile Legends Professional League (MPL) has become a focus for competitive teams around the world. This study aimed to determine the classification of victory in MPL matches based on draft pick. Gaussian Naïve Bayes and Decision Tree were used as classification algorithm models in this study. The process in this study included cleaning data, data transformation (labeling), handling imbalanced data, scaling, splitting, and hyperparameter. The evaluation stage used confusion matrix, correlation data, and AU-ROC curve. The results of this study showed that the Decision Tree method had better performance than Gaussian Naïve Bayes in classifying data using the confusion matrix. The AUC (area under the receiver operating characteristic curve) analysis showed that the decision tree had better performance than Gaussian naive Bayes in predicting positive and negative data. This is indicated by the higher AUC value for the Decision Tree, which is 0.67 compared to Gaussian Naïve Bayes which is 0.48. Classification models with higher AUC values can more accurately distinguish between positive and negative data. In this study, the Decision Tree had a higher AUC value than Gaussian Naïve Bayes so the Decision Tree could more accurately classify victory and defeat data.

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