Abstract
There are currently an estimated 1.5 million species of mushrooms in the world. Among the millions of mushrooms that exist throughout the world, there are two types of mushrooms, namely edible and poisonous mushrooms. Many people get food poisoning because they don't know that the mushrooms are poisonous. Even some countries have reported cases of poisoning due to poisonous mushrooms. However, identifying edible and poisonous mushrooms is not easy because of the large number of mushrooms and has similar characteristics. Identifying the type of mushrooms can be done by utilizing data mining science, namely classification, which can help find essential patterns from millions or even billions of data records. The method used to classify the types of mushroom is using the K-Nearest Neighbor and Decision Tree methods. The performance of the two methods was compared so that it can be seen which method is better in classifying the type of fungus. Experimental analysis conducted on the UCI Mushroom dataset provides evidence of the proposed method's effectiveness and the most appropriate method for the classification of mushroom types. The results obtained indicate that the Decision Tree method has better performance with an accuracy value of 0.9193 or 91.93%, a precision of 0.9227, recall of 0.9193, and an F1 score of 0.9210.
Published Version
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