OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM) UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS
Oman Somantri, Slamet Wiyono, Dairoh Dairoh · Telematika · 2017
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 %