Eko Prasetyo


The use of data mining in the past 2 decades in harnessing the data sets become important. This is due to the information given outcome becomes very important, but the big problem are the obstacles data mining task is a very large amount of data. A very large number indeed specificity of data mining in extracting information, but the amount of too big data also cause decrease the performance. On the issue of classification, data that are not positioned on the decision boundary becomes less useful and make classification method is not efficient. K-Nearest Neighbor Support Vector present to answer the problem that data is normally owned by very large data. K-SVNN able to reduce the amount of very large data with good accuracy without degrading performance. Results of performance comparisons with a number of classification method also proves that K-SVNN can provide good accuracy. Among the five comparison methods, K-SVNN got in the big 3 methods. K-SVNN difference accuracy to other methods less of 0.66% on the data set Iris and 20:29% on the data set Wine.

Teks Lengkap:



Freund, Y., and Schapire, R. E., (1996), Experiments with a New Boosting Algorithm, in Proceeding of the 13th International Conference on Machine Learning, Bari, Italy, 325-332

Lazarevic, A., and Obradovic, Z., (2001). Data Reduction Using Multiple Models Integration , In Proceeding of Principles of Data Mining and Knowledge Discovery, Freiburg, 301-313.

Prasetyo, E., (2012). K-Support Vector Nearest Neighbor Untuk Klasifikasi Berbasis K-NN, in Proceeding of Seminar Nasional Sistem Informasi Indonesia, Institut Teknologi Sepuluh Nopember, Surabaya.

Prasetyo, E., R.A.D. Rahajoe, S. Agustin, A. Arizal, (2013). Uji Kinerja dan Analisis K-Support Vector Nearest Neighbor Terhadap Decision Tree dan Naive Bayes, Eksplora Informatika, 3(1), 1-6.

Prasetyo, E., (2014). Data Mining – Mengolah Data Menjadi Informasi Menggunakan Matlab, Andi Offset, Yogyakarta.

Prasetyo, E., S. Alim, H. Rosyid, (2014). Uji Kinerja dan Analisis K-Support Vector Nearest Neighbor dengan SVM dan ANN Back-Propagation, in Proceeding Seminar Nasional Teknologi Informasi dan Aplikasinya, Politeknik Negeri Malang, Malang

Prasetyo, E., (2015). Reduksi Data Latih Dengan K-svnn Sebagai Pemrosesan Awal pada ANN Back-Propagation Untuk Pengurangan Waktu Pelatihan, SIMETRIS, 6(2), 223-230

Provost, F., Jensen, D., Oates, T., (1999), Efficient Progressive Sampling, in Proceeding of Fifth International Conference On Knowledge Discovery and Data Mining, 23-32.

Shih, L., Rennie, J.D.M., Chang, Y.H., Karger, D.R., (2003). Text Bundling: Statistics-Based Data Reduction, in Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC

Tan, P.N., M. Steinbach, V. Kumar, (2006), Introduction to Data Mining, 1st Ed, Pearson Education: Boston San Fransisco New York.

UCI Machine Learning Repository , 1 Juni 2014, http://archive.ics.uci.edu/ml/datasets.html


  • Saat ini tidak ada refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.