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dc.contributor.advisorMarbun, James Piter
dc.contributor.advisorSitompul, Opim Salim
dc.contributor.authorNurdin, Nurdin
dc.date.accessioned2023-01-24T04:48:13Z
dc.date.available2023-01-24T04:48:13Z
dc.date.issued2012
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/80877
dc.description.abstractPrediction of student achievement in this research only predict based on national test scores by using the trainbp method and the trainbpx method. This research uses the input patterns of the subjects tested on the national exam for high school students, namely: Mathematics, Indonesian language, English, Physics/Economics, Chemistry/Sociology, and Biology/Geography, while the pattern of the desired output or target is divided into two patterns, namely the pattern (0) assuming no student achievement and patterns (1) assuming achievement students. Then these values are trained and tested for the system to recognize it properly. After training and testing, the neurons that are considered worth more than 0.5000 reputed well worth 1 (achievement), whereas neurons that are considered worth less than 0.5000 reputed well worth 0 (no achievement). The results of the training trainbp method and trainbpx method of 150 training data with the results reached 100%. Test results of the testing (outcome prediction) of 100 with trainbp method with the results of 33 data (33%) achieved a prediction accuracy (in accordance with the target) and 67 data (67%) did not correspond with the target, whereas with trainbpx method, the data showed 69 (69%) achieved a prediction accuracy, and as many as 31 data (31%) did not succeed, because incompatible with the target.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectneural networken_US
dc.subjectmethod trainbpen_US
dc.subjectmethod trainbpxen_US
dc.titlePrediksi Prestasi Mahasiswa Berdasarkan Nilai Ujian Nasional Menggunakan Jaringan Saraf Tiruan dengan Metode TrainBP dan TrainBPXen_US
dc.typeThesisen_US
dc.identifier.nimNIM097038015
dc.identifier.nidnNIDN0011065803
dc.identifier.nidnNIDN0017086108
dc.identifier.kodeprodiKODEPRODI55101#TeknikInformatika
dc.description.pages76 Halamanen_US
dc.description.typeTesis Magisteren_US


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