Perbandingan Kinerja Sistem Klasifikasi Berbasis K-Fold Cross Validation Pada Algoritma Decision Tree ID3 Dan C5.0

Muhammad Yusuf Ashari, Abd. Charis Fauzan, Huda Maariful Muhamat

Abstract

In data mining, many techniques and methods have been carried out in predictive models, classification methods for example, one of which is the Decision Tree or Decision Tree including ID3, C4.5 C5.0 and others. In this study, the authors evaluate the performance of the classification and comparison system of the ID3 Decision Tree Algorithm with the C5.0 Decision Tree Algorithm, where the C5.0 Decision Tree Algorithm is an extension of the C4.5 Decision Tree Algorithm and the ID3 Decision Tree Algorithm based on the K-Fold Cross Algorithm. Validation. These algorithms need to be compared to find out which algorithm has the best performance and will be used to predict the data. Therefore, in this research the aim is to compare the ID3 Decision Tree Algorithm with the C5.0 Decision Tree Algorithm. In this research, 215 datasets of the feasibility of labor placement are used. This research AIur starts from data collection, pre-processing, calculation of the ID3 and C5.0 Decision Tree Algorithms and then evaluated using K-fold Cross Validation. The results of this study indicate, through a comparison of the performance of the K-fold Cross Validation-based classification system, the ID3 Decision Tree Algorithm is superior to the C5.0 Decision Tree Algorithm. Decision Tree ID3 algorithm with 95% precision, 94% recall/sensitivity and 93% accuracy. While the Decision Tree C5.0 Algorithm with 91% precision, 92% recall and 89% accuracy.

Keywords

Decision Tree ID3; Decision Tree C5.0; K-fold Cross Validation;

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