PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG
Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the co...
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| Format: | Academic Paper |
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2014-10-30.
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| Online Access: | http://eprints.undip.ac.id/46344/ |
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| LEADER | 01845 am a22001693u 4500 | ||
|---|---|---|---|
| 001 | repository_undip_46344_ | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a OCTAVIANI, PUSPHITA ANNA |e author |
| 245 | 0 | 0 | |a PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG |
| 260 | |c 2014-10-30. | ||
| 500 | |a http://eprints.undip.ac.id/46344/1/PUSPHITA_ANNA.pdf | ||
| 520 | |a Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the compulsory basic education, should have the status of accreditation to ensure the quality of education. This study aimed to apply the classification method Support Vector Machine (SVM) on the data accreditation SD in Magelang. Support Vector Machine (SVM) is a method that can be used as a predictive classification by using the concept of searching hyperplane (separator functions) that can separate the data according to the class. SVM using the kernel trick for non-linear problems which can transform data into a high dimensional space using a kernel function, so that the data can be classified linearly. The results of this study indicate that the prediction accuracy of SVM classification using Gaussian kernel function RBF is 93.902%. It is calculated from 77 of 82 elementary schools that are classified correctly with the original classes. Keywords : Accreditation, Classification, Support Vector Machine (SVM), hyperplane, Gaussian RBF Kernel, Accuracy | ||
| 690 | |a HA Statistics | ||
| 655 | 7 | |a Thesis |2 local | |
| 655 | 7 | |a NonPeerReviewed |2 local | |
| 787 | 0 | |n http://eprints.undip.ac.id/46344/ | |
| 856 | 4 | 1 | |u http://eprints.undip.ac.id/46344/ |