Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost - M. Khaerul Ihsan; Muhammad Maulana; Tanwir Tanwir; Abi Mas’ud; Naufal Hanif; Dading Oktaviadi Resmiranta (Universitas Bumigora, 2025)
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 improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical 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 itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency.