Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Yunita Ardilla, Wilda Imama Sabilla, Nurissaidah Ulinnuha


Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.


algoritma ant tree miner; algoritma c4.5; k-fold cross validation; data mining; klasifikasi

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