49
DAFTAR PUSTAKA
Adriani, M., & Nazief, B. 1996. Confix-Stripping: Approach to Stemming Algorithm for Bahasa Indonesia. Faculty of Computer Science University of Indonesia.
Adriani, M., Asian, J., Nazief, B., Tahaghoghi, S.M.M., & Williams H.E. 2007. Stemming Indonesian: A Confix-Stripping Approach. Journal ACM Transactions on Asian Language Information Processing 6(4): 1-33.
Agusta, L. 2009. Perbandingan Algoritma Stemming Porter Dengan Algoritma Nazief & Adriani Untuk Stemming Artikel Teks Bahasa Indonesia. Prosiding Konferensi Nasional Sistem dan Informatika 2009, pp. 196-201.
Alodokter. 2016. Alodokter - Daftar Penyakit. (Online) http://www.alodokter.com/penyakit-a-z (Diakses 2 Agustus 2016)
Al-Marghilani, A., Zedan, H. & Ayesh, A. 2008. Text Mining Based on Self Organizing Map Method for Arabic-English Documents. Proceedings of the Nineteenth Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2008), pp. 174-181.
Amalia, Gunawan, D., Najwan, A., & Meirina, F. 2016. Focused Web Crawler for the Acquisition of Health Articles. Proceeding of International Conference on Data and Software Engineering, pp. 47-52.
Ambarwati & Winarko, E. 2014. Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map. Indonesian Journal of Computing and Cybernetics Systems 8(1): 101-110.
Berry, W.M. & Kogan, J. 2010. Text Mining: Applications and Theory. Wiley: Padstow.
Gatial, E., Balogh, Z., Laclavik, M., Ciglan, M. and Hluchy, L. Focused Web Crawling Mechanism based on Page Relevance. Proceedings Information Technologies - Applications and Theory, Peter Vojtas (Ed.), pp.41-46.
Gutwin, C., Paynter, G., Witten, I., Nevill-Manning, C., and Frank, E. 1999. Improving Browsing in Digital Libraries with Keyphrase Indexes. Decision Support Systems, 27: 81–104.
Hulth, A., & Megyesi, B.B. 2006. A Study on Automatically Extracted Keywords in Text Categorization. Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 537-544.
50
Husni, Negara, Y.D.P., & Syarief. M. 2015. Clusterisasi Dokumen Web (Berita) Bahasa Indonesia Menggunakan Algoritma K-Means. Jurnal SimanteC 4(3): 159-166.
Ikatan Dokter Anak Indonesia, 2015. IDAI - Public Articles. (Online) http://www.idai.or.id/artikel (Diakses 11 Juli 2016).
Kateglo, 2016. Kateglo ~ Kamus, tesaurus, dan glosarium bahasa Indonesia. (Online) http://www.kateglo.com/ (Diakses 1 Agustus 2016).
Kementerian Kesehatan Republik Indonesia, 2013. Kementerian Kesehatan Republik
Indonesia - Kamus. (Online)
http://www.depkes.go.id/folder/view/full-content/structure-kamus.html (Diakses 21 Juli 2016)
Kohonen, T. 1995. Self-Organizing Maps. Springer: Berlin.
Prasetyo, E. 2012. Konsep dan Aplikasi Menggunakan MATLAB. Andi: Yogyakarta.
Robertson, S., 2004. Understanding Inverse Document Frequency: On theoretical arguments for IDF. Journal of Documentation 60(5): 503-520.
Samodra, Sumpeno & Hariadi. 2009. Klasifikasi Dokumen Teks Berbahasa Indonesia dengan Menggunakan Naïve Bayes. Seminar Nasional Electrical, Informatics, and IT Education 2009, pp. B1-71 – B1-74.
Suryaningsih, V. 2015. Clustering Dokumen Menggunakan Algoritma Self Organizing Maps. Skripsi. Universitas Negeri Sebelas Maret.
Tala, F.Z. 2003. A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia. Tesis. Institute for Logic, Languange and Computation. Universiteti van Amsterdam the Netherlands.
Turney, P. 1999. Learning to Extract Keyphrases from Text. National Research Council Canada, Institute for Information Technology, Technical Report ERB-1057
Vijayarani, S., & Janani, R. 2016. Text Mining: Open Source Tokenization Tools – An Analysis. Advanced Computational Intelligence: An International Journal (ACII) 3(1): 37-47.
Yang, H.-C., Chen, D.-W., Lee, & C.-H. 2007. Mining Multilingual Texts Using Growing Hierarchical Self-Organizing Maps. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics 2007, pp. 2263-2268.
Yusuf, A. & Priambadh, T. 2013. Support Vector Machines Yang Didukung K-Means Clustering Dalam Klasifikasi Dokumen. Jurnal Ilmiah Teknologi Informasi 11(1): 13-16.
