Analisis Kinerja Algoritma C4.5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan

- This study describes the application of the algorithm C4.5 on decision support systems to support trainees in PPTIK STIKI Malang in choosing the appropriate type of training. Decision support system based on several criteria derived from the data filled out by participants prior to register as a participant. Further analysis using an algorithm that is used to form a C4.5 decision tree. The decision tree is a method of classification and prediction that represent rules. the rule is then developed using RGFDT (Rule Generation From Decision Tree). Results of testing done by comparing the system with Weka and showed an accuracy of 90%.Keywords—Algorithm C4.5, Decision Support System, RGFD

Analisis Kinerja Algoritma C4.5 Pada Sistem Pendukung Keputusan Penentuan Jenis Pelatihan

Abstract - This study describes the application of the algorithm C4.5 on decision support systems to support trainees in PPTIK STIKI Malang in choosing the appropriate type of training. Decision support system based on several criteria derived from the data filled out by participants prior to register as a participant. Further analysis using an algorithm that is used to form a C4.5 decision tree. The decision tree is a method of classification and prediction that represent rules. the rule is then developed using RGFDT (Rule Generation From Decision Tree). Results of testing done by comparing the system with Weka and showed an accuracy of 90%. Keywords—Algorithm C4.5, Decision Support System, RGFD

Rules Generation from the Decision Tree

[[abstract]]To avoid checking unnecessary or irrelevant conditions of rules, the irrelevant values problem of the decision tree is addressed. We propose an algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to the semantics of the decision tree. Since our algorithm depends only on the semantics of the decision tree, our algorithm can be integrated into any existing tree-construction algorithm with negligible increase in computational cost concerning that of constructing the decision tree. Moreover, as a side effect, the resultant rules are less likely to suffer from missing branches problem