Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach

  • Fadhila Tangguh Admojo Universiti Kuala Lumpur
  • Made Leo Radhitya Institut Bisnis dan Teknologi Indonesia
  • Hamada Zein Universitas Muhammadiyah Kalimantan Timur
  • Ahmad Naswin Universitas Megarezky Makassar

Keywords: Binary Classification, Data Pre-processing, Food Safety, K-Nearest Neighbors, Mushroom Classification

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.

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Published
2024-12-31
How to Cite
Admojo, F. T., Radhitya, M. L., Zein, H., & Naswin, A. (2024). Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach. Indonesian Journal of Data and Science, 5(3), 243-250. https://doi.org/10.56705/ijodas.v5i3.199