ANALISA PERFORMA ALGORITMA MACHINE LEARNING DALAM PREDIKSI PENYAKIT LIVER
DOI:
https://doi.org/10.35870/jimik.v4i1.149Keywords:
Machine Learning, Support Vector Machine, Naïve Bayes, K-Nearest NeighborAbstract
Currently in the world of medicine, determining liver inflammation is something that is not easy to do. But there are medical records that have kept the patient's symptoms and diagnosis of liver inflammation. The weaknesses of the manual method encourage researchers to develop a method that does not depend 100% on humans. The developed method utilizes a computer as a tool to analyze data. This kind of thing is certainly very useful for health experts. They can use existing medical records as an aid in making decisions about the diagnosis of a patient's disease. In this study, we analyzed the performance of machine learning algorithms by comparing the support vector machine, naïve Bayes and k-nearest neighbor algorithms. This study aims to determine the performance of which algorithm has the highest accuracy in liver disease data. From the research results using splinting data 80:20 it can be concluded that the Naïve Bayes algorithm model has better performance than other algorithm models when using the SMOTE technique with an accuracy value of 65.51%, whereas when not using the SMOTE technique the Support Vector Machine algorithm has the highest performance. better than other algorithm models with an accuracy value on the data not 72.41%.
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