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All Journal Xplore: Journal of Statistics
Mulianto Raharjo
Kementerian Dalam Negeri Republik Indonesia, Indonesia
Decision Sciences, Operations Research & Management Engineering Mathematics

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Perbandingan Performa Metode Pohon Model Logistik dan Random Forest pada Pengklasifikasian Data Purnama Sari; Kusman Sadik; Mulianto Raharjo
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.078 KB) | DOI: 10.29244/xplore.v12i1.858

Abstract

Multicollinearity and missing data are two common problems in big data. Missing data could decrease the prediction accuracy. Logistic model tree (LMT) is used to handle multicollinearity because multicollinearity does not affect the decision tree. Random forest can be used to decrease variance in prediction case. This study aimed to study the comparison of two methods, LMT and random forest, in multicollinearity and missing data in various cases using simulation study and real data as dataset. Evaluation model is based on classification accuracy and AUC measurement. The result stated that random forest had better performance if the multicollinearity level is moderate. LMT with omitted missing data is proven to have better performance for big data and when a high percentage of missing data occurred, and the multicollinearity level is severe. The next step is analysed real data with different sample size. The result stated that random forest have better performance. Omitted missing data have better performance in classification “breast cancer” data which consist 0,3 % missing data.
Co-Authors Kusman Sadik Purnama Sari