Abstract
A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time. 
Highlights
have many student data in their database because many students did not graduate on time
implementation of Support Vector Machine (SVM) algorithm to model the prediction of student graduation on time
modeling was used by implementing data training and data testing on SVM algorithm and evaluation
Summary
Kelulusan mahasiswa tepat waktu merupakan hal yang paling penting dari suatu universitas. Data mining merupakan suatu yang juga menggunakan SVM untuk menentukan model kumpulan teknik yang digunakan sebagai bagian dari terbaik dalam prediksi kelulusan mahasiswa adalah [4]. Objek data mining merupakan data dalam jumlah Penelitian mengenai penerapan algoritma SVM untuk banyak dan komplek dan tujuan dari data mining mendapatkan model prediksi kelulusan mahasiswa tepat mencari hubungan atau bentuk yang dapat memberikan waktu juga dilakukan oleh [7]. Beberapa peneliti telah menggunakan SVM untuk algoritma tersebut menghasilkan bahwa algoritma SVM melakukan prediksi waktu kelulusan mahasiswa seperti mampu memberikan tingkat akurasi yang paling baik yang dilakukan oleh [5] sebagai metode klasifikasi. Penelitian yang dilakukan oleh [6] menggunakan sepuluh algoritma machine learning dalam menentukan model untuk memprediksi kelulusan tepat waktu bagi calon mahasiswa, salah satunya adalah SVM. Penelitian yang dilakukan oleh [11] membandingkan algoritma support vector machine (SVM), neural
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