Optimasi Decision Tree menggunakan Particle Swarm Optimization untuk klasifikasi sel Pap Smear - Toni Arifin (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2020)
This research presents the classification of Pap Smear cells. The data used is the Herlev dataset which amounts to 917. This research aims to determine whether the use of particle swarm optimization algorithm can improve the performance of the Decision tree algorithm in classifying Pap Smear Cell data. The stages of this research are preprocessing, feature optimization, knowledge rules, evaluation and performance report. The results of this research are the Decision Tree algorithm produces an accuracy of 91.39% with AUC 0.858, While the application of particle swarm optimization algorithm in Decision tree resulted in better accuracy of 96.76% with AUC 0.926.