Optimasi Support Vector Machine untuk Memprediksi Adanya Mutasi pada DNA Hepatitis C Virus
Abstract
Hepatitis C Virus (HCV) is a virus which capable of infecting RNA that can lead to changes in the DNA sequence. This change of DNA arrangement is called genetic mutation. Every mutation occurs in HCV, it will be called a new subtype. Over time, HCV subtypes increase, and will continue to grow as the HCV mutation cycle progresses faster. Therefore, a way to find a mutation in millions of sequences in the gene bank is needed. This study tested six types of Support Vector Machine (SVM) methods to determine the best SVM kernel performance in the application of HCV DNA sequence detection in isolatedDNA. The tested SVM kernel was linear, quadratic, cubic, fine Gaussian, median Gaussian, and coarse Gaussian. The data set is 1000 isolated DNA consisting of 500 isolated Homo Sapiens and 500 isolated HCV. Firstly, the data set will go through the pattern search process using the Edit Levenshtein Distance method, then the result of the processing will be the variable x in SVM. The target or variable y on SVM is the positive or negative value of the isolated against HCV. The results show that among the six types of SVM methods being tested, the method of fine Gaussian SVM has the lowest performance of 77.4%. The SVM method is tested by performing optimizations on the determination of the hyperplane. The test results proved that the SVM method is able to analyze the presence of HCV mutations in isolated DNA with an accuracy of 99.8%.
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