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연구정보

[사회] Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet

인도네시아 국외연구자료 학술논문 Ednawati Rainarli, Arif Romadhan Journal of Information Systems Engineering and Business Intelligence 발간일 : 2017-12-31 등록일 : 2018-04-06 원문링크

A coach must be able to select which athlete has a good prospect of winning a game. There are a lot of aspects which influence the athlete in winning a game, so it's not easy by coach to decide it.This research would compare Simple Logistic Classifier (SLC) and Support Vector Machine (SVM) usage applied to predict winning game of athlete based on health and physical condition record. The data get from 28 sports. The accuracy of SLC and SVM are 80% and 88% meanwhile processing times of SLC and SVM method are 1.6 seconds dan 0.2 seconds.The result shows the SVM usage superior to the SLC both of speed process and the value of accuracy. There were also testing of 24 features used in the classifications process. Based on the test, features selection process can cause decreasing the accuracy value. This result concludes that all features used in this research influence the determination of a victory athletes prediction.

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