Implementasi Algoritma Random Forest Untuk Mendeteksi Penyakit Multiple Sclerosis
Keywords:
Algoritma Random Forest, Klasifikasi, Multiple SclerosisAbstract
Multiple sclerosis (MS) is a neurodegenerative autoimmune disease that can cause neurological effects and disorders. Based on the latest data from the 3rd edition of Atlas MS in 2020 issued by the Multiple Sclerosis International Federation, the number of sufferers of the disease (MS) globally continues to increase from 2.3 million sufferers in 2013 to 2.8 million sufferers in 2020. In addition Therefore, the increase in new cases is increasingly varied, even children under 18 years of age have increased compared to previous case findings, making (MS) considered the main representative example of an autoimmune disease. The development of increasingly sophisticated machine learning technology is often used to detect disease. In this research, the random forest algorithm was implemented to detect (MS) through several stages, such as the preprocessing stage, then the random forest algorithm classification stage by building a model using a proportion of 80%:20% comparison of training data and test data to build 100 classification models. tree using bootstrap aggregating, random feature selection, and entropy methods. The results of implementing the random forest algorithm to detect (MS) were accumulated according to the majority vote and evaluation of the confusion matrix test resulted in an accuracy value of 81%, precision of 80%, recall of 85%, specificity of 77%, and f1-score of 82%. The results of this research show that the classification model using the random forest algorithm that was developed has good performance.
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