Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil
DOI:
https://doi.org/10.30865/mib.v6i1.3373Keywords:
Classification, Wart Disease, K-Nearest Neighbor, Random ForestAbstract
Warts are a skin health problem, usually characterized by small, rough bumps on the surface of the skin caused by a virus, known as the human papillomavirus (HPV). The common way of treating warts is with immunotherapy, which is the treatment of warts by strengthening the body's immune system. In the process of predicting and diagnosing warts, it can be done by applying Machine Learning. This study focuses on the comparison of the K-Nearest Neighbor classification method with Random Forest to see the level of accuracy in predicting the success of the treatment of warts. Data for Immunotheraphy was obtained from the UCI Machine Learning Repository with a total of 90 data records, 7 attributes and 1 attribute class. Based on the results of testing the K-Nearest Neighbor and Random Forest methods to see the accuracy of the prediction of the success of the data being tested, the results obtained are the accuracy of the KNN method of 90.00% and the Random Forest method with an accuracy of 85.50%. From the results obtained from the tests that have been carried out, it is known that the Random Forest method is a better method than K-Nearest Neighbor in predicting accuracy in the Immunotheraphy Dataset.References
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