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dc.contributor.advisorPRASETYO, EKO
dc.contributor.advisorRIYADI, SLAMET
dc.contributor.authorMARDHANI, ARYA
dc.date.accessioned2017-10-30T03:02:03Z
dc.date.available2017-10-30T03:02:03Z
dc.date.issued2017-06-20
dc.identifier.urihttp://repository.umy.ac.id/handle/123456789/15602
dc.descriptionKelulusan tepat waktu sangatlah penting bagi mahasiswa, program studi serta perguruan tinggi. Beberapa mahasiswa yang telah lulus di analisis untuk menjadi acuan prediksi mahasiswa yang masih duduk di bangku perkuliahan, sehingga mampu memprediksi jumlah mahasiswa yang dapat lulus tepat waktu kedepannya. Proses analisis data menggunakan teknik data mining dan algoritma Naive Bayes. Data yang digunakan mahasiswa angaktan 2010-2011 yang telah lulus dan belum lulus sebagai data training sebanyak 435 record. Angkatan 2011 dan 2014 yang diambil secara acak sebagai data testing sebanyak 100 record dari database Universitas Muhammadiyah Yogyakarta. Software yang digunakan RapidMiner, Sql Server 2014 management studio dan micrososft excel.en_US
dc.description.abstractTimely graduation is very important for students, study programs and universities. Some students who have passed the analysis to be a prediction of students who are still sitting in the lecture, so as to predict the number of students who can pass on time in the future. The process of data analysis using data mining techniques and Naive Bayes algorithm. Data used by students of 2010-2011 who have graduated and have not passed as data training as much as 435 record. Force 2011 and 2014 are taken at random as data testing as many as 100 records from the database of Muhammadiyah University of Yogyakarta. Software used RapidMiner, Sql Server 2014 studio management and excel micrososften_US
dc.publisherFT UMYen_US
dc.subjectTimely graduation, data mining, Naïve Bayes, Rapidminer. Prediksi Kelulusan Tepat waktu, data mining, Naive Bayes, RapidMiner.en_US
dc.titlePENERAPAN DATA MINING DALAM PREDIKSI KELULUSAN TEPAT WAKTU MENGGUNAKAN ALGORITMA NAIVE BAYES UNIVERSITAS MUHAMMADIYAH YPGYAKARTA.en_US
dc.typeThesis SKR F T 571en_US


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