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Mustagfirin Mustagfirin
Department of Informatics Engineering, Universitas Wahid Hasyim, Indonesia
Computer Science & IT

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Performance of the Decision Tree Algorithm in the Classification of Edible and Poisonous Mushrooms with Information Gain Optimization Arif Rifan Rudiyanto; Pujiono Pujiono; M. Arief Soeleman; Mustagfirin Mustagfirin
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47864

Abstract

Purpose: This study proposes a new mushroom classification model using a decision tree algorithm to classify edible and poisonous mushrooms by applying machine learning whose algorithm has better performance in terms of accuracy.  Methods: The information gain technique was applied at the data feature selection stage to increase the accuracy of the suggested decision tree model. This study used the same mushroom dataset as that employed in previous studies. Result: The proposed decision tree model in this study can classify edible and poisonous mushrooms with a good accuracy of 99.61%, outperforming a previous study whose final accuracy was 97.05%. Novelty: The novelty of this s is the use of information gain as a filter technique at the feature selection stage. This study aims to optimize the previous mushroom classification models with improved accuracy.
Co-Authors Arif Rifan Rudiyanto M. Arief Soeleman Pujiono Pujiono