Classification algorithm for edible mushroom identification

Indonesia has 13% species of mushroom in the world but there is a very limited study on determining edible or poisonous mushroom. Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowled...

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Bibliographic Details
Published in:	2018 International Conference on Information and Communications Technology (ICOIACT) pp. 250 - 253
Main Authors:	Wibowo, Agung, Rahayu, Yuri, Riyanto, Andi, Hidayatulloh, Taufik
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-03-2018
Subjects:	c4.5 Classification algorithm Classification algorithms Data mining Decision trees Mushroom Naive Bayes Prediction algorithms Support Vector Machine Support vector machines Testing Training data
Online Access:	Get full text
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Summary:	Indonesia has 13% species of mushroom in the world but there is a very limited study on determining edible or poisonous mushroom. Classification process of poisonous mushroom or not will be easily conducted by learning machine using mining data as one of the ways to extract computer assisted knowledge. Currently, there are three comparisons of the best classification algorithms in data mining, namely: Decision Tree (C4.5), NaïveBayes and Support Vector Machine (SVM). The study method used is experiment with assisted tool of WEKA that has been testing in the comparison of the three algorithms. To conduct the testing, it is used the mushroom data of Agaricus and Lepiota family. The mushroom data were taken from The Audubon Society Field Guide to North American Mushrooms, in UCI machine learning repository. Results of the testing indicate that the C4.5 algorithm has the same accuracy level to the SVM by 100% however, from the speed aspect, process of the C4.5 algorithm is faster than the SVM.
DOI:	10.1109/ICOIACT.2018.8350746