Vol 13 No 2 (2016): Edisi Juli 2016
General

OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM) UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

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  • Oman Somantri,
  • Slamet Wiyono,
  • Dairoh Dairoh
Oman Somantri
Program Studi Teknik Informatika Politeknik Harapan Bersama Tegal
Slamet Wiyono
Program Studi Teknik Informatika Politeknik Harapan Bersama Tegal
Dairoh Dairoh
Program Studi Teknik Informatika Politeknik Harapan Bersama Tegal
DOI: https://doi.org/10.31315/telematika.v13i2.1722

Cara Mengutip

Somantri, O., Wiyono, S., & Dairoh, D. (2017). OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM) UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS. Telematika, 13(2), 59–68. https://doi.org/10.31315/telematika.v13i2.1722

Abstrak

The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM ) and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %
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