Pembangunan Sistem Manajemen Pengetahuan Menggunakan Pendekatan Clustering untuk Ekstraksi Pengetahuan
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Abstract
The importance of knowledge management in any organization encourages the development of a knowledge management system with features that can facilitate knowledge management processes such as storing, organizing, filtering, searching, and most important is the transfer of knowledge. The purpose of this research is to develop a knowledge management system with a clustering approach for knowledge extraction by using a knowledge of publication writing. This study uses clustering k-means method which is used for cluster knowledge feature where at the same time can help the process of organizing, filtering, browsing and searching knowledge. The results of this research showed that the clustering k-means can be used for knowledge management system with the best value of purity = 0,8454 which is found by using k = 20. Clustering approach in the system again can help the process for knowledge searching based on knowledge cluster. This can be proved by carried out 15 times experiments which result in average level of accuracy (precision) about 89.13% and the average rate of completeness (recall) about 85.73 %.
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References
Baets, W., 2005. Knowledge Management and Management Learning. Springer, USA.
Boahene, M., Ditsa, G., 2003. Conceptual Confusions in Knowledge Management and Knowledge Management Systems: Clarifications for Better KMS Development. IRM PRESS, Australia.
Butler, T., Feller, J., Pope, A., Emerson B., Murphy C., 2008. Designing a Core IT Artefact for Knowledge Management Systems using Participatory Action Research in A Government and A Non-government Organisation, Journal of Strategic Information Systems 17 (4), 249 – 267.
Can, F., E. A., 1990. Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases. ACM Transactions on Database Systems 15 (4), 483 - 517.
Darawaty, I.S., Syarah, S., Nugroho, A.H., Ayuningtyas, F., Istianto, Y., Prasetyo, B., Uliniansyah, M., Gunawan, M., Ani, D., Jarin, A., Handoko, D., 2010. Intelligent Searching Using Association Analysis for law Documents of Indonesian Government. Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, Jakarta, December 2-3, 122-124.
Kumar, V., Steinbach, M., Tan, P., 2004. Introduction to Data Mining. Addison-Wesley.
Kurniawan, C., 2012. Perancangan Knowledge Management System PT. XYZ. Master Thesis, Graduate Program-Binus University, Jakarta.
Larose, D.T., 2005. Discovering Knowledge in Data : An Introduction to Data Mining. Wiley-Interscience, Canada.
Maier, R., 2007. Knowledge Management Systems - Information and Communication Technologies for Knowledge Management : Third edition. Springer, New York.
Manning, C.D., Raghavan P, dan Schütze H., 2009. Introduction to Information Retrieval. Cambridge University Press.
Mardia. K., Kent., Bibby., 1979. Multivariate Analysis, Academic Press.
Silwattananusarn, T., Tuamsuk, K., 2012. Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012. International Journal of Data Mining & Knowledge Management Process (IJDKP) 2 (5), 13 – 24.
Srividhya, R., Anitha, R., 2010. Evaluating Preprocessing Techniques in Text Categorization. International Journal of Computer Science and Application Issue, 49 -51.
Suhartono, E., 2013. Sistem Klasifikasi dan Pencarian Dokumen Teks dengan Algoritma Naïve Bayes Classifier dan Vector Space Model. Master Thesis, Program Pascasarjana Universitas Diponegoro, Semarang.
Wibisono, Y., 2011. Perbandingan Partition Around Medoids (PAM) dan K-means Clustering untuk Tweets, Prosiding Konferensi Nasional Sistem Informasi. Medan, Februari 25-26, 483 – 486.
Xiong, H., Wu, J., dan Chen, J., 2009. K-Means Clustering Versus validation Measures: A Data Distribution Perspective, IEEE Transactions on Systems, Man, and Cybernetics – Part B 39 (2), 318-331.