ANALISIS PERBANDINGAN ALGORITMA DECISION TREE DENGAN SUPPORT VECTOR MACHINE UNTUK MENDETEKSI KOMPETENSI MAHASISWA KONSENTRASI INFORMATIKA KOMPUTER STUDI KASUS : POLITEKNIK LP3I JAKARTA, KAMPUS DEPOK
Abstract
Detection of computer informatics student competence is indispensable for anticipating students who have a very poor performance in following the learning process in an educational institution for the purpose of all educational institutions are creating a qualified student. It can be seen in the results of the 5th and 6th semester students who have gained employment. Polytechnic LP3I Jakarta Depok one vocational education institution founded to create a human being who has the ability / skills required by the company so that the concept is to offer education that have Link and Match. Competitors who have the same goals is one of the challenges to be faced by the agency so we need a solution to overcome it. One solution is the detection of computer informatics student competence of students. This can be done by using data mining techniques. One data mining techniques used are support vector machines (SVM). Support vector machine method is able to overcome the problem of high-dimensional, addressing the problem of classification and regression with linear or nonlinear kernel that can be the ability of learning algorithms for classification and regression, but the support vector machine has a problem in the appropriate parameters. To overcome these problems required method of decision tree as a comparison, for the selection of appropriate parameters. Several experiments were conducted to obtain optimum accuracy. Experiments using support vector machine and decision tree which is used to optimize the parameters C, and ε population. Training data used computer informatics student data from 2012 to 2014 academic year. The experimental results show the decision tree method of data that is equal to 92.50% with a ratio of 60 training data were compared with data vector machine that is equal to an accuracy of 92.56% and the second T-Test metod done that method has a probability value of < 0.05 which algorithm C4.5.
Keywords: Detection, Competence, Support Vector Machine, Decision Tree
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Ahmad Syafiq, Sandra Fikawati (22 Feb 2007).Seminar Terbuka “Kompetensi Yang Dibutuhkan Dalam Dunia Kerja”(Hasil Tracer Study FKM UI Tahun 2006), Ruang Sidang Doktor Gedung G FKMUI
A. Basuki and I. Syarif, Decision Tree.Surabaya: Politeknik Elektronika Negeri Surabaya- ITS, 2003.
Ahmed. (2014). Data Mining : A Prediction for Student’s Perfomance Using Classification Method. World Journal of Computer Aplication and Technology 2 , (2) 43-47.
Azwar, S. (2004). Penyusunan Skala Psikologi. Yogyakarta: Pustaka Pelajar.
BAN-PT. (2011). Akreditasi Institusi Perguruan Tinggi - Buku III Pedoman Penyusunan Borang.
Basari, A. S. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering , 53, 453–462. doi:10.1016/j.proeng.2013.02.059.
Berndtssom, M. H. (2008). A Guide For Students In Computer Science And Information Systems. London: Springer.
Dawson, C. W. (2009). Projects In Computing And Information System A Student's Guide. England: Addison - Wesley.
Ernastuti, S. &. (2010). Graduation Prediction of Gunadarma University Students Using Algorithm and Naive Bayes C4.5 Algoritmh.
Gorunescu, F. (2011). Data Mining: Concepts and Techniques. Verlag berlin Heidelberg: Springer.
Han, J. &. (2007). Data Mining Concepts and Techniques. San Fransisco: Mofgan Kaufan Publisher.
Haupt, R. L. (2004). Practical Genetic Algorithms. United State of America: A John Wiley & Sons Inc Publication.
Hermawati, F. (2013). Data Mining. Yogyakarta: Andi Offset.
Ispandi. (2014). Penerapan Algoritma Genetika untuk Optimasi Parameter pada Support Vector Machines Untuk Meningkatkan Prediksi Pemasaran Langsung. In TESIS. STMIK Nusa Mandiri.
Jacobus, A. ( 2014). Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time. IJCCS , Vol.8, No.1, January 2014, pp. 13~24.
Jacobus, A. (2014). Penerapan Metode Support Vector Machine pada Sistem Deteksi Intrusi secara Real-time. IJCCS , Vol.8, No.1 January 2014 pp. 13~24.
Jing Wang, P. L. (2014). Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM. E-Health Telecommunication Systems and Networks , 9-17.
Kabag Marketing & C N P. (2014). Laporan Perkembangan Mahasiswa LP3i Depok TA. 2009 s/d 2013. Depok.
Kopertis3. (2014). Peraturan Perundangan. Retrieved june 10, 2014, from http://www.dikti.go.id/id/peraturan-perundangan/
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. John Willey & Sons. Inc.
Liu, Y. W. (2011). An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, , 8(2), 191–200. doi:10.1016/S1672-6529(11)60020-6.
M. N. Quadri and N. V Kalyankar, “Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques,”Glob. J. Comput. Sci. Technol., vol. 10, no. 2, pp.2–5, 2010.
Maimon, O. (2010). Data Mining And Knowledge Discovery Handbook. New York Dordrecht Heidelberg London: Springer.
Nugroho, A. S. (2008). Support Vector Machine: Paradigma Baru Dalam Softcomputing. Konferensi Nasional Sistem Dan Informatika , 92-99.
Oyelade, A. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k-means Clustering algorithm for predicting of Students Academic Performace. International Journal of Computer Science and Information Security , 292-295.
Polancic, G. (2007). Empirical Research Method Poster.
Pramudiono, I. (2006). Retrieved january 16, 2007, from Apa itu data mining?,: http://datamining.japati.net/cgi-bin/indodm.cgi?bacaarsip&1155527614&artikel
Riduwan. (2008). Metode dan Teknik Menyusun Tesis. Bandung: Alfabeta.
Santosa, B. (2007). Data Mining Teknik Pemanfaat Data Untuk Keperluan Bisnis. Yogyakarta : Graha Ilmu.
Saraswati, N. W. (2014). Text mining dengan metode naïve bayes classifier dan support vector machines untuk sentiment analysis.
Satsiou, a. (2002). Genetic Algorithms for the Optimization of Support Vector Machines in Credit Risk Rating,.
Shao, S. (2014). Construction and Application of Performance Prediction Model for Aerobics Athletes Based on Online-SVM. International Journal of Hybrid Information Technology , Vol.7, No.4 (2014), pp.43-54.
Siregar. (2006). Motivasi Belajar Mahasiswa ditinjau dari Pola Asuh. Medan: USU : Repository.
Sugiyono, P. (2011). Metode Penelitian Kuantitatif Kualitatif dan R & D. Bandung: Alfabeta.
Sujana. (2002). Metode Statistika. Bandung: PT. Tarsito.
Tekin, A. (2014). Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach. Eurasian Journal of Educational Research , Issue 54, 2014, 207-226.
Turban, E. d. (2005). Decicion Support Systems and Intelligent Systems. Andi Offset.
Vercellis, C. (2009). Business Intelligence Data Mining And Optimization For Decision Making. United Kingdom: A John Wiley And Sons, Ltd., Publication.
Vrettos, K. &. (2008). An Artificial Neural Network for Predicting Student Graduation Outcomes. Preceeding of World Congress on Engineering and Computer Science. 978-988-98671-02.
Witten, H. I., Eibe, F., & Hall, A. M. (2011). Data Mining Machine Learning Tools and Techiques. Burlington: Morgan Kaufmann Publisher.
Wu, X. &. (2009). The Top Ten Algorithms in Data Mining. Boca Raton: CRC Press.
Yenaeng, S. (2014). Automatic Medical Case Study Essay Scoring by Support Vector Machine and Genetic Algorithms. IJIET , Vol. 4, No. 2.
Yingkuachat, J., Praneetpolgrang, P., & Kijsirikul, B. (2007). An Application Probabilitic Model to the Prediction of Student Graduation Using Bayesian Belief Network. ECTI Transaction on Computer and Technology , 63-71.
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