Perbaikan Performansi Klasifikasi Dengan Preprocessing Iterative Partitioning Filter Algorithm - Djoko Budiyanto Setyohadi; Felix Ade Kristiawan; Ernawati Ernawati (Telematika, 2017)
Preprocessing data and preprocessing performance analysis are crucial in datamining. Those two points have great impact to data mining process success rate, because aquality decisions must be based on quality data. Preprocessing is useful to increase the qualityof data and to reduce the noise data. Our experiment show that the performance iterativepartitioning filter algorithm is tested by using some dataset from University of California, Irvine(UCI) Machine Learning Repository and is simulated by using modified iterative partitioningfilter's parameter variation. This experiment also explained how to analyze classification resultfrom a preprocessed dataset using Backpropagation, so that it can identify best accuracy frommultiple datasets that have been tested. Final result from this experiment is table of data consistof training time, classification accurarcy, classification error, Kappa statistic, Mean AbsoluteError (MAE) or average of iterations error, Root mean squared error and confusion matrix. Thisfinal result is presented in ratio chart between experiment result and modified iterativepartitioning filter's parameter