Analisa Splitting Criteria Pada Decision Tree dan Random Forest untuk Klasifikasi  Evaluasi Kendaraan

Arie  Nugroho

doi:10.53624/jsitik.v1i1.154

Authors

Arie Nugroho Universitas Nusantara PGRI Kediri

DOI:

https://doi.org/10.53624/jsitik.v1i1.154

Keywords:

Splitting Criteria, Decision Tree, Random Forest, Klasifikasi

Abstract

Klasifikasi adalah salah satu topik dalam data mining. Algoritma atau model yang termasuk dalam klasifikasi antara lain Decision tree, K-NN, Naïve bayes. Decision tree merupakan model yang mudah untuk dipahami karena dapat divisualisasikan. Random Forest adalah salah satu model dalam klasifikasi yang merupakan pengembangan dari decision tree. Pemilihan splitting criteria dalam decision tree dan random forest dapat mempengaruhi hasil akurasi. Dalam artikel ini memaparkan perbandingan splitting criteria dalam model klasifikasi dengan decision tree dan random forest untuk data evaluasi kendaraan. Dengan menggunakan split data dan cross validation serta pengujian dengan confusion matrix, pemilihan splitting criteria memberikan pengaruh pada nilai akurasi dari model yang telah dihasilkan.

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