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All Journal Indonesian Journal of Statistics and Its Applications
Hasnita Hasnita
Department of Statistics, IPB University, Indonesia
Computer Science & IT Mathematics Other

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PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK Hasnita Hasnita; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.328

Abstract

One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.
Co-Authors Farit Mochamad Afendi Fitrianto, Anwar