Perbandingan Algoritma C4.5 dan Classification and Regression Tree (CART) Dalam Menyeleksi Calon Karyawan

Ng Poi Wong, Florida N. S. Damanik, Christine -, Edward Surya Jaya, Ryan Rajaya


This research compares the accuracy of the C4.5 algorithm and Classification and Regression Tree (CART) for prospective employees selection in companies. This research using dataset with criteria like age, working experience, recent education, marital status, number of abilities possessed, and the result of admission selection test. Testing uses 200 prospective employee selection data manually from a company. Algorithm testing using K-Fold Cross Validation and the accuracy calculation of the algorithm using Confusion Matrix. C4.5 algorithm has a level of accuracy, the success rate of the system, and the level of accuracy of the decision results of 52,83%, 41,48% and 43,98%, and CART algorithm is 53,33%, 44,06%, and 42,81%.


Accuracy, C4.5, Classification and Regression Tree (CART)

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