Application of Decision Tree Algorithm for Edible Mushroom Classification

Afika Rianti; Taufik Ridwan; Suprih Widodo; Rian Andrian

doi:10.30871/jaic.v6i1.4087

Afika Rianti Universitas Pendidikan Indonesia
Taufik Ridwan Universitas Pendidikan Indonesia
Suprih Widodo Universitas Pendidikan Indonesia
Rian Andrian Universitas Pendidikan Indonesia

DOI: https://doi.org/10.30871/jaic.v6i1.4087

Keywords: Classification, Decision Tree Algorithm, Edible Mushroom, Overfitting, Random Sampling Techniques

Abstract

The purpose of this research is to classify the mushroom based on its characteristic to be in an edible class or poisonous one using the Decision Tree Algorithm. The result showed that odor is the most important attribute to classify the mushroom. Mushrooms which have almond and anise odors are edible, while the rest of it, such as pungent, foul, creosote, fishy, spicy, and musty are poisonous which means they can't be eaten. For mushrooms that have no odor, there are some attributes to be checked such as spore-print-color, gill-size, gill-spacing, and population. At first, overfitting happened. To overcome this, the researcher used Random Sampling Techniques until got better accuracy. The most accurate sample is 99,9% using sample 6 or 2000 data.

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