Prediksi Spesies Burung Menggunakan Random Forest

 (*)Anisha Dwi Nur Fadlilah Mail (Universitas Katolik Darma Cendika, Surabaya, Indonesia)
 Yulia Wahyuningsih (Universitas Katolik Darma Cendika, Surabaya, Indonesia)
 Yosef Alfredo Khawarga (Universitas Katolik Darma Cendika, Surabaya, Indonesia)

(*) Corresponding Author

Abstract

Randomforest is a supervised learning algorithm that uses ensemble learning methods for classification and regression. Random forest is a modeling using pocketing technique and not increasing technique. trees in random forests run in parallel. It operates by constructing multiple decision trees at the time of training and class output which is the mode of class (classification) or mean prediction (regression) of each tree. In this journal we will explain how to make a prediction about bird species using performance data from bird species for which we make predictions using the Random forest method and also using a confusion matrix. The first thing to do is to have a dataset of bird species. For the dataset, we will include it in the results and discussion. The results of this study show that the use of the random forest is good enough to predict bird species with output 45% for random forest, 26% for Decision Tree, and 48% for SVM, based on the characteristics and characteristics of each bird.

Keywords


Random Forest;confusion matrix;decision tree;SVM

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Copyright (c) 2022 Anisha Dwi Nur Fadlilah, Yulia Wahyuningsih, Yosef Alfredo Khawarga

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