Improving the Classification Performance of Bovine ScabiesUsing Edited Nearest Neighbours (ENN) on Random Forestand XGBoost Models
DOI:
https://doi.org/10.30812/bite.v7i2.6055Keywords:
Classification, Cattle Scabies, Edited Nearest Neighbours, Machine Learning, Random Forest, XGBoostAbstract
Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’
physical condition and productivity.
Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improving
classification performance for scabies in cattle.
Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600
clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.
Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing it
from around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability to
detect positive cases.
Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnostic
system. This method is highly recommended to optimize the performance of classification algorithms on animal clinical
data with high levels of inconsistency.
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Copyright (c) 2026 M. Khaerul Ihsan, Muhammad Maulana; Tanwir Tanwir; Abi Mas’ud, Naufal Hanif, Dading Oktaviadi Resmiranta
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