Open AccessComparation Analysis of Ensemble Technique With Boosting(Xgboost) and Bagging (Randomforest) For Classify Splice Junction DNA Sequence Category
Author(s) -
Iswaya Maalik Syahrani
Publication year - 2019
Publication title -
jurnal penelitian pos dan informatika
Language(s) - English
Resource type - Journals
eISSN - 2476-9266
pISSN - 2088-9402
DOI - 10.17933/jppi.v9i1.249
Subject(s) - random forest , boosting (machine learning) , computer science , computation , decision tree , ensemble learning , sequence (biology) , artificial intelligence , data mining , pattern recognition (psychology) , machine learning , algorithm , biology , genetics
Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.