Abstract
The purpose of this study is to study which is better between the Decision Tree (C4.5) Algorithm and the k-Nearest Neighbor (k-NN) algorithm based on Particle Swarm Optimization (PSO) based on an assessment of its accuracy on passing the right delivery time. The results showed the closest k-Neighbor (k-NN) algorithm based on PSO at k-optimum = 19 had better results than the Decesion Tree (C4.5) algorithm, with an Accuracy value of 76.69% for the k- algorithm NN and 69.82% for the Decesion Tree Algorithm (C4.5). Increasing the accuracy value of the k-NN algorithm (6.88%) is greater than the Decesion Tree Algorithm (5.80%). Semester Achievement Index Attribute (IPS) 6 has the highest weighting value with the Decesion Tree Algorithm (C4.5) and the k-NN Algorithm. The Age attribute has a weighting value of 0,000 meaning it does not make a real contribution to the right graduation of student time. Keywords: Accuracy, Algorithm, Decision Tree (C4.5), k-Nearest Neighbor (k-NN), Particle Swarm Optimization (PSO)
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