Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
KLASIFIKASI PENERIMA BEASISWA KOPERTIS DENGAN MENGGUNAKAN ALGORITMA C.45
Keywords:
Algorithm C4.5, Beasiswa Kopertis, Clasification
Abstract
Every year college get scholarship coordinator private universities (kopertis), Scholarship devoted to student in a college namely scholarship an increase in academic performance (scholarship PPA ) and scholarship assistance learn student (scholarship BBM). The process filing scholarship ppa and bbm through two stages selection selection is the first stage selection in college to determine a candidate scholarship recipients that would be proposed to kopertis, the selection that both the stage in kopertis selection. Many a student who submitted the scholarships as well as surpassing kouta given resulted in the process of select recipients taken longer since select must be in accordance with the criteria so that the recipient scholarship right on target . Based on these problems need a the act of determining scholarship recipients proper. The purpose of this research is to make classifications students scholarship recipients with algorithm C4.5. The results of classifications evaluate and validated with confusion matrix and a curve ROC, the results classifications students scholarship recipients namely algorithm C4.5 with the level of accuracy of 86.88 %, So that it can be applied for the problem the determination of scholarship recipients .
Downloads
Download data is not yet available.
References
Gorunescu, F. 2011. Data Mining Concepts, Models and Techniques. Berlin: Springer.
Han, J., & Kamber, M. 2006. Data Mining Concepts And Techniques 2nd Edition. San Fransisco: Elsevier.
Jones, P. 2001. Visual Basic: A Complete Course. London: Continuum.
Karismariyanti, M. 2011. Simulasi Pendukung Keputusan Penerima Beasiswa Menggunakan Metode Composite Performance Index. Jurnal Teknologi Informasi Vol.1 , 54-59.
Kotsiantis, S., & Pintelas. P. 2004. A Decision Support Prototype Tool for Predicting Student Performance in an ODL Environment. Greece: University of Patras.
Larose, D. T. 2005. Discovering Knowledge in Data An Introduction to Data Mining. New Jersey: John Willey and Sons.
Liao, T. W. 2007. Enterprise Data Mining: A Review and Research Directions. Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications , 1-109.
Liberty, J. 2005. Programming Visual Basic 2005. USA: O'Reilly Media.
Maimon, O., & Rokach, L. 2010. Data Mining and Knowledge Discovery Handbook Second Edition. London: Springer.
Powers, D. 2011. Evaluation: From Precision, Recall and F-Measure To ROC, Informedness, Markedness & Correlation. Journal of Machine
Learning Technologies , 37-63.
Santiary, P. A. 2012. Sistem Pendukung Keputusan Cerdas dalam Penentuan Beasiswa. Jurnal Logic Vol.12 No.2 , 87-91.
Suryana, T. 2009. Visual Basic. Yogyakarta: Graha Ilmu.
Vercellis, C. 2009. Business Intelligence. United Kingdom: John Wiley and Sons.
Witten, I. H., Frank, E., & Hall, M. A. 2011. Data Mining Practical Machine Learning Tools and Techniques Third Edition. Burlington: Elsevier.
Wu, X., & Kumar, V. 2009. The Top Ten Algorithms in Data Mining. USA: CRC Press.
www.dikti.go.id/.januari-13,2014.
http://www.dikti.go.id/files/Lemkerma/kepmen232-2000.txt
Han, J., & Kamber, M. 2006. Data Mining Concepts And Techniques 2nd Edition. San Fransisco: Elsevier.
Jones, P. 2001. Visual Basic: A Complete Course. London: Continuum.
Karismariyanti, M. 2011. Simulasi Pendukung Keputusan Penerima Beasiswa Menggunakan Metode Composite Performance Index. Jurnal Teknologi Informasi Vol.1 , 54-59.
Kotsiantis, S., & Pintelas. P. 2004. A Decision Support Prototype Tool for Predicting Student Performance in an ODL Environment. Greece: University of Patras.
Larose, D. T. 2005. Discovering Knowledge in Data An Introduction to Data Mining. New Jersey: John Willey and Sons.
Liao, T. W. 2007. Enterprise Data Mining: A Review and Research Directions. Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications , 1-109.
Liberty, J. 2005. Programming Visual Basic 2005. USA: O'Reilly Media.
Maimon, O., & Rokach, L. 2010. Data Mining and Knowledge Discovery Handbook Second Edition. London: Springer.
Powers, D. 2011. Evaluation: From Precision, Recall and F-Measure To ROC, Informedness, Markedness & Correlation. Journal of Machine
Learning Technologies , 37-63.
Santiary, P. A. 2012. Sistem Pendukung Keputusan Cerdas dalam Penentuan Beasiswa. Jurnal Logic Vol.12 No.2 , 87-91.
Suryana, T. 2009. Visual Basic. Yogyakarta: Graha Ilmu.
Vercellis, C. 2009. Business Intelligence. United Kingdom: John Wiley and Sons.
Witten, I. H., Frank, E., & Hall, M. A. 2011. Data Mining Practical Machine Learning Tools and Techniques Third Edition. Burlington: Elsevier.
Wu, X., & Kumar, V. 2009. The Top Ten Algorithms in Data Mining. USA: CRC Press.
www.dikti.go.id/.januari-13,2014.
http://www.dikti.go.id/files/Lemkerma/kepmen232-2000.txt
Published
2016-03-07
How to Cite
Tabrani, M. (2016). KLASIFIKASI PENERIMA BEASISWA KOPERTIS DENGAN MENGGUNAKAN ALGORITMA C.45. Jurnal Pilar Nusa Mandiri, 12(1), 72-80. https://doi.org/10.33480/pilar.v12i1.261
Article Metrics
Abstract viewed = 453 times
PDF downloaded = 456 times
Abstract viewed = 453 times
PDF downloaded = 456 times
Section
Articles
Copyright (c) 2016 Muhamad Tabrani
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
