Optimasi Support Vector Machine untuk Memprediksi Adanya Mutasi pada DNA Hepatitis C Virus

Berlian Al Kindhi, Tri Arief Sardjono, Mauridhi Hery Purnomo

Abstract


Hepatitis C Virus (HCV) adalah virus yang mampu menginfeksi RNA sehingga dapat mengakibatkan perubahan susunan DNA. Perubahan susunan DNA inilah yang disebut dengan mutasi genetik. Setiap terjadi mutasi pada HCV, akan disebut dengan subtipe baru. Semakin lama, subtipe HCV semakin banyak, dan akan terus bertambah seiring dengan semakin cepatnya siklus mutasi HCV. Oleh karena itu, dibutuhkan sebuah cara yang dapat menemukan adanya mutasi pada jutaan sequence di dalam bank gen. Makalah ini menguji coba enam jenis metode Support Vector Machine (SVM) untuk mengetahui kinerja terbaik kernel SVM dalam penerapan deteksi sequence DNA HCV di dalam isolated DNA. Kernel SVM yang diuji yaitu linear, quadratic, cubic, fine Gaussian, median Gaussian, dan coarse Gaussian. Data set yang digunakan adalah 1000 isolated DNA yang terdiri atas 500 isolated Homo Sapiens dan 500 isolated HCV. Data set tersebut akan melalui proses pencarian pola terlebih dahulu menggunakan metode Edit Levenshtein Distance, kemudian hasil dari pengolahan tersebut akan menjadi variabel x pada SVM. Target atau variabel y pada SVM adalah nilai positif atau negatifnya isolated DNA tersebut terhadap HCV. Hasil penelitian menunjukkan bahwa dari keenam jenis metode SVM yang diujikan, metode fine Gaussian SVM memiliki kinerja paling rendah, yaitu sebesar 77.4%. Metode SVM diuji coba dengan melakukan optimasi pada penentuan hyperplane-nya. Hasil uji coba membuktikan bahwa metode SVM mampu menganalisis adanya mutasi HCV pada isolated DNA dengan menghasilkan akurasi sebesar 99,8%.

Keywords


SVM; Mesin Pembelajaran; Sequence DNA; Semantic Similarity

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DOI: http://dx.doi.org/10.22146/jnteti.v7i3.441

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