Klasifikasi Helpdesk Menggunakan Metode Support Vector Machine

Stefanie Hilda Kusumahadi, Hartarto Junaedi, Joan Santoso

Abstract


The online helpdesk with ticketing system with the help of operators often experiences problems such as inappropriate delegation processes, the duration of the helpdesk waiting time to be delegated, even the helpdesk is missed to be handled. The ticket delegation checked manually by the operator has risks creating an error in delegating helpdesk tickets to inappropriate technicians. The helpdesk classification system is needed so that every incoming helpdesk ticket can be classified to the right technician according to the job description. The incoming Helpdesk is classified into 6 types of requests, namely multimedia, documentation, internet, server, hardware, software and miscellaneous. This helpdesk grouping is needed so that related technicians for each helpdesk can work and help the helpdesk according to their respective job descriptions. The Support Vector Machine method is used to classify text on the helpdesk. The use of Linear and Polynomial kernels produces an accuracy of 78%, the RBF or Gaussian kernel produces the highest accuracy of 81% while the Sigmoid kernel produces the smallest accuracy of 51%. The helpdesk classification results with the Support Vector Machine method can produce quite good accuracy.


Keywords


Machine Learning; Support Vector Machine; Text Mining

Full Text:

References


M. Altintas and A. C. Tantug, “Machine Learning Based Tiket Classification in Issue Tracking Systems,” Proceeding Int. Conf. Artif. Intell. Comput. Sci. (AICS 2014), no. September, pp. 195–207, 2014.

N. Goby, T. Brandt, S. Feuerriegel, D. Neumann, and C. Research Goby, “Business Intelligence for Business Processes: the Case of It Incident Management,” ECIS Proc., no. April, pp. 1–15, 2016.

I. Pilászy, “Text Categorization and Support Vector Machines,” vol. 1, pp. 1–10, 2004.

D. Wang, T. Li, S. Zhu, and Y. Gong, “IHelp: An intelligent online helpdesk system,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 41, no. 1, pp. 173–182, 2011.

Informatikalogi, “Text Preprocessing,” 2016. [Online]. Available: https://informatikalogi.com/text-preprocessing/. [Accessed: 05-Dec-2018].

J. Santoso et al., “Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia,” JNTETI, vol. 7, no. 2, pp. 158–166, 2018.

K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. A. Kurgan, Data Mining A Knowledge Discovery Approach, vol. 30, no. 11. 2007.

R. Munawarah, O. Soesanto, and M. R. Faisal, “Penerapan Metode Support Vector Machine Pada Diagnosa Hepatitis,” Kumpul. J. Ilmu Komput., vol. 04, no. 01, pp. 103–113, 2013.

A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support Vector Machine , Teori dan Aplikasinya dalam Bioinformatika,” Wiley Interdiscip. Rev. Comput. Stat., vol. 3, no. 3, pp. 204–215, 2011.

S. Lee and H. BYUN, “A survey on pattern recognition applications of support vector machines,” Int. J. Pattern Recognit., vol. 17, no. 3, pp. 459–486, 2003.

É. D. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, “Scikit-learn: Machine Learning in Python,” 2011. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

V. Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, P. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, A. and and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, and E. Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, “Scikit Learn: Support Vector Machine.” [Online]. Available: https://scikit-learn.org/stable/modules/svm.html. [Accessed: 14-Jan-2019].

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques. 2011.

R. Diani, U. N. Wisesty, and A. Aditsania, “Analisis Pengaruh Kernel Support Vector Machine ( SVM ) pada Klasifikasi Data Microarray untuk Deteksi Kanker,” Ind. J. Comput., vol. 2, pp. 109–118, 2017.

Antoni Wibowo, “10 Fold-Cross Validation,” 2017. [Online]. Available: https://mti.binus.ac.id/2017/11/24/10-fold-cross-validation/. [Accessed: 16-Nov-2018].




DOI: http://dx.doi.org/10.30591/jpit.v4i1.1125

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JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.