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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi
Oman Somantri
Program Studi Teknik Informatika Politeknik Harapan Bersama Tegal
Electrical & Electronics Engineering

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OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM) UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS Oman Somantri; Slamet Wiyono; Dairoh Dairoh
Telematika Vol 13, No 2 (2016): Edisi Juli 2016
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v13i2.1722

Abstract

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 %
Co-Authors Dairoh Dairoh, Dairoh Slamet Wiyono