Detail Prediction of student academic performance by an application of data mining techniques (presented at 2011 International Conference on Management and Artificial Intelligence IPEDR vol.6 (2011) Bali, Indonesia Bibliografi Author: Sembiring, Sajadin ; Zarlis, Muhammad ; Hartama, Dedy ; Ramliana S. ; Wani, Elvi Topik: DM; SSVM; kernel-k-means; Student Performance Bahasa: (EN )     Penerbit: International Association of Computer Science and Information Technology Press (IACSIT)     Tempat Terbit: Bali, Indonesia    Tahun Terbit: 2011     Jenis: Papers/Makalah - pada seminar internasional Fulltext: 21-A10015.pdf (782.78KB; 0 download) [Informasi yang berkaitan dengan koleksi ini di internet] Abstract One of the significant facts in higher learning institution is the explosive growth educational data. These data are increasing rapidly without any benefit to the management. The main objective of any higher educational institution is to improve the quality of managerial decisions and to impart quality education. Good prediction of student’s success in higher learning institution is one way to reach the highest level of quality in higher education system. There are many prediction model available with difference approach in student performance was reported by researcher, but there is no certainty if there are any predictors that accurately determine whether a student will be an academic genius, a drop out, or an average performer. The aims of this study was to apply the kernel method as data mining techniques to analyze the relationships between students’s behavioral and their success and to develop the model of student performance predictors . This is done by using Smooth Support Vector Machine (SSVM) classification and kernel k-means clustering techniques. The results of this study reported a model of student academic performance predictors by employing psychometric factors as variables predictors. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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