Perbandingan Reduced Support Vector Machine dan Smooth Support Vector Machine untuk Klasifikasi Large Data

Epa Suryanto, Santi Wulan Purnami
Submission Date: 2015-02-02 14:10:25
Accepted Date: 2015-03-16 09:16:43

Abstract


Klasifikasi merupakan pengelompokan objek ke dalam dua atau lebih kelompok yang didasarkan pada variabel yang diamati. Support Vector Machine merupakan metode berbasis machine learning yang sangat menjanjikan untuk dikembangkan karena memiliki performansi tinggi dan dapat diaplikasikan secara luas untuk klasifikasi dan estimasi. SVM memanfaatkan optimasi dengan quadratic programming, sehingga untuk data berdimensi tinggi dan berjumlah besar, SVM menjadi kurang efisien. Untuk mengatasi hal tersebut, dikembangkan Smooth Support Vector Machine (SSVM). Pada jumlah data yang besar SSVM juga tidak efisien kemudian dikembangkan Reduced Support Vector Machine (RSVM) yang melakukan klasifikasi dengan menggunakan sebagian karakteristik dari data yang dipilih secara random. Hasil penelitian ini menunjukkan pada jumlah data yang relatif kecil (kurang dari 1000) metode SSVM dan RSVM memberikan performansi yang sama, tetapi pada data yang relatif besar (lebih dari 1000) RSVM memberikan performansi yang lebih baik daripada SSVM.

Keywords


Klasifikasi;Smooth Support Vector Machine;Reducedd Smooth Support Vector Machine;Large Data

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