DECISION TREE DENGAN BINARY BAT ALGORUTHM OPTIMIZATION PADA HEART CATHETERIZATION PREDICTION - Junita Amalia; Natasya Yosevin Nababan; Kharisma Grace Tambunan; Indah Sonia Sinaga (Hexagon Jurnal Teknik dan Sains, 2022)
Risk of heart catheterization is huge, to ensure the patient will perform right heart catherization optimally, it is necessasry to predict whether the paient needs it or not. In this study, the classification is using K-Nearest Neighbor, Decision Tree C4.5, and Decision Tree C4.5 optimized with Bat Binary Algorithm. Prior to classification, data prepocessing is carried out which aims to prodoce good data for processing and obtain accurate classification result. The data prepocessing carried out are data cleaning, data selection and data normalization. The combination of the best parameter value for Binary Bat Algorithm as the feature generator selected is q = 2, a = 0.2, r = 0.4, n = 30, Mi = 10 and the comparison of traning and testing data are 90% and 10%. Based on experiment result, the classification accuracy of Decision Tree optimized with Bat Binary Algorithm is the highest, which is 0.725. Meanwhile the accuracy of K-Nearest Neighbor is 0.600 and accutacy of decision Tree is 0.588.