PENGGUNAAN METODE SUPPORT VECTOR MACHINE UNTUK MENGKLASIFIKASI DAN MEMPREDIKSI ANGKUTAN UDARA JENIS PENERBANGAN DOMESTIK DAN PENERBANGAN INTERNASIONAL DI BANDA ACEH

Sayed Fachrurrazi, Burhanuddin Burhanuddin

Abstract


Penelitian ini menyajikan analisis performansi Support Vector Machine
(SVM) dengan 11 variabel bebas dan 1 variabel terikat. Metode SVM
dengan data training (75%) dan data testing (25%) yang digunakan pada
pengklasifikasian data Penerbangan domestic dan data penerbangan
internasional untuk menemukan hyperplane terbaik yang memisahkan
dua buah kelas. Hasilnya terdapat 4 support vector memberikan informasi
yang dibutuhkan untuk menyakinkan bahwa metode SVM bias sebagai
classifier dan dapat memprediksi keakuratan model dengan menggunakan
kurva Receiver Operating Characteristic (ROC) untuk melihat akurasi model
terbaik. mencapai 84,31%.

Kata Kunci: Klasifikasi, Metode Support Vector Machine (SVM),
Receiver Operating Characteristic (ROC)

 


Full Text:

PDF

References


Beeza-Yates, Ricada & Berthier Ribeiro-Neto. 1999., Modern

Information Retrieval. Addison Wesley.

Brefeld, Ulf., 2005, AUC Maximizing Support Vector Learning,

Chin, 1998, Using a Radial Basis Function as Kernel, http://svrwww.eng.cam.ac.uk/-kkc21/thesi

main/node31.html.K.K

Christianini, N. And Shawe Taylor, J., 2000, An Introduction to

Support Vector Machine and other Kernel Based Learning methods,

Cambridge University Press.

Cover Knowledge Bese_2006. Understanding Stemiry,

http.//www.Cover. com/and support/articles/informationCES4-060330-3-Understanding, Steming.

Dimitriadou, Evgenia, Hornik, Kurt, Leisch, Friedrich, Meyer,

David, and Andreas, Weingessel, 2007, E1071 : Misc Functions

of the Department of Statistics (e1071), TU Wien. R package

version 1.5-17.

Feldman, Susan. 2004, Why Categorize http://www.kmworld.com/Articles/Editorial/Feature/whycategorize

f-9580.aspx

Goller, C et. Al. 2000. Automatic Document Classification : A Thorough

Evaluation of Various Methods.

Han, J. and Kamber, M. 2006. Data Mining Concepts and Techniques,

Second Edition. Morgan Kauffman. San Francisco.

Joachims, Thorstem,. 1999. Transductive Inference for Text ClassiCation

Using Support Vector Machines. Procecdings of the

International and Comference.on Machine learning (ICMC)

Karatzoglou, Alexandros., 2006, Support Vector Machines in R,

http://www.jstatsoft.org/ MedCalc Software bvba.__.ROC Curve Analysis: Introduction.

MedCalc Software, Broekstraat 52, 9030 Mariakerke. Belgium.

http://www.medcalc.be/manual/roc.php.

Nugroho, Anto Satrio., 2003, Support Vector Machine Teori dan

Aplikasinya dalam Bioinformatika.

Pramudiono, I. 2007, Pengantar Data Mining : Menambang Permata

Support Vector Machine Untuk Mengklasifikasi Dan Memprediksi

Angkutan Udara Pengetahuan di Gunung Data.

http://www.ilmucomputer.org/wpcontent/uploads/2006/08

/IKOdatamining.zip.

Rainardi, Vincent. 2008. Building a Data Warehouse with Examples in

SQL Server. Springer. New York.

R.Goldstein, Darlene. 2003. ROC Curves, Swiss Institute of

Bioinformatics, Switzerland

Santosa, Budi. 2007. Data Mining Teknik Pemanfaatan Data untuk

Keperluan Bisnis. Graha Ilmu. Yogyakarta.

Sebastiani, Fabrizio.2002. Machine Learning in Automated Text

Categorization. ACM Computing Surveys, 34 (1) : 1-47

http://nmis.isti.cnr.it/sebastiani/publications/ACMCS02.pdf.

Sembiring, Krisantus. 2007. Tutorial SVM Bahasa Indonesia. Skripsi,

S1 Teknik Informatika, Teknik Elektro dan Informatika, ITB.

Bandung

Sun, Aixin, 2002. Web Classification Using Support Vector Machine.

Proceedings of the 4th

Int. Workshop on Web Information and

Data Management (WIDM 2002) held in conj. With CIKM 2002,

Virginia. USA.

Tuszynski, Jarek. 2007. caTools : Tools : moving window statistics, GIF,

Base64, ROCAUC, etc.. R package version 1.8.

Vapnik, V, 1995. The Nature of Statistical Learning Theory, SpringerVerlag.

Witten, I. H and Frank, E. 2005. Data Mining : Practical Machine

Learning Tools and Techniques. Second Edition. Morgan

Kauffman. San Francisco




DOI: https://doi.org/10.29103/sisfo.v2i2.1008

Article Metrics

 Abstract Views : 1931 times
 PDF Downloaded : 713 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Sayed Fachrurrazi, Burhanuddin Burhanuddin

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

 


 
 

Universitas Malikussaleh		  

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