Kusumaningrum, Retno

Surarso, Bayu

WIDJANARKO, Agus

English

Diponegoro University Institutional Repository

   77    DAFTAR PUSTAKA  Adriani, M., Asian, J., Nazief, B. dan Williams, H., 2007. Stemming Indonesian  A Confix-Stripping Approach. ACM Transactions on Asian Language Information, 6(4), pp. 1-33. Aggarwal, C. dan Zhai, C., 2013. Mining text data. New York: Springer. Al-Saleh, A. dan Menai, M. E. B., 2016. Automatic Arabic text summarization: a survey. Artificial Intelligence Review, 45(2), p. 203–234. Arora, R. dan Ravindran, B., 2008. Latent Dirichlet Allocation Based Multi Document. Singapore, Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data. Badan Pengembangan dan Pembinaan Bahasa, 2016. Kamus Besar Bahasa Indonesia (KBBI). 4 edisi . Jakarta: Kementerian Pendidikan dan Kebudayaan. Blei, D., 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, Issue 3, p. 993–1022. Blei, D., Carin, L. dan Dunson, D., 2010. Probabilistic topic models. IEEE Signal Processing Magazine, 27(6), p. 55–65. Chang, Y. L. dan Chien, J., 2009. Latent Dirichlet learning for document summarization. Taipei, Taiwan, ICASSP, IEEE International Conference on Acoustics, Speech, and Signal Processing. Fattah, M., 2014. A hybrid machine learning model for multi-document summarization. Applied Intelligence, 40(4), pp. 592-600. Fattah, M. dan Ren, F., 2009. GA, MR, FFNN, PNN and GMM based models for automatic text summarization. Computer Speech and Language, 23(1), pp. 126-144. Gambhir, M. dan Gupta, V., 2016. Recent automatic text summarization techniques:a survey. Artificial Intelligence Review, 47(1), pp. 1-66. Gorunescu, F., 2011. Data Mining. San Francisco: Morgan Kaufmann.  78  Gunawan, F. E., Juandi, A. V. dan Soewito, B., 2015. An automatic text summarization using text features and singular value decomposition for popular articles in Indonesia language. s.l., International Seminar on Intelligent Technology and Its Applications Proceeding, pp. 27-32. Gupta, V. K. dan Siddiqui, T. J., 2012. Multi-document summarization using sentence clustering. s.l., 4th International Conference on Intelligent Human Computer Interaction: Advancing Technology for Humanity. Heinrich, G., 2009. Parameter estimation for text analysis. s.l., The European Conference on Machine Learning and Principles and Pratice of Knowledge Discovery in. Hernández, G. dkk., 2008. Text Summarization by Sentence Extraction Using Unsupervised Learning. In: Lecture Notes in Computer Science. Berlin: Springer, pp. 133-143. Indu, M. dan K.V, K., 2016. Review on Text summarization evalution methods. Bengaluru, International Conference on Research Advances in Integrated Navigation Systems, pp. 06-07. Juan, M. dan Moreno, T., 2014. Evaluating Document Summaries, In Automatic Text Summarization. Hoboken: John Wiley dan Sons, Inc.. Korenius, T., Laurikkala, J., Järvelin, K. dan Juhola, M., 2004. Stemming and lemmatization in the clustering of finnish text documents. Beijing, Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, p. 625–633. Kusumaningrum, R., Wei, H., Manurung, R. dan Murni, A., 2014. Integrated visual Vocabulary in Latent Dirichlet Allocation–Based Scene Classification for IKONOS image. Journal of Applied Remote Sensing, 8(1), pp. 83690-1-18. Kusumaningrum, R., Wiedjayanto, M., Adhy, S. dan Suryono, 2016. Classification of Indonesian News Articles based on Latent Dirichlet Allocation. Yogyakarta, Indonesia, IEEE International Conference on on Data and Software Engineering Proceedings. Li, G. dan Liu, F., 2010. A Clustering-based Approach on Sentiment Analysis. Barcelona, International Conference on Intelligent Systems and Knowledge Engineering.  79  Liu, N., Tang, X.-J., Lu, Y. dan Wang, H., 2014. TopicSensitive Multi-document Summarization Algorithm. s.l., Sixth International Symposium on Parallel Architectures, Algorithms and Programming. Liu, Z., 2013. High Performance Latent Dirichlet Allocation, London: Brunei University. Lukmana, . I. dkk., 2014. Multi-document summarization based on sentence clustering improved using topic words. Jurnal Ilmiah Teknologi Informasi, 12(2), pp. 1-8. Na, L. dkk., 2016. Multi-document summarization algorithm based on significance sentences. Beijing, Proceedings of the 28th Chinese Control and Decision Conference, p. 3847–3852. Pei, Y., Yin, W. dan Huang, L., 2012. PRICAI 2012: Trends in Artificial Intelligence. In: Pacific Rim International Conference on Artificial Intelligence. Berlin: Springer-Verlag, p. 435–446. Ponweiser, M., 2012. Latent Dirichlet Allocation in R, Vienna: Vienna University of Economics and Business. Rukmana, S. dkk., 2014. Summarizing Text for Indonesian Language by Using Latent Dirichlet Allocation and Genetic Algorithm. s.l., Proceeding of the Electrical Engineering, Computer Science. Saggion, H. dan Poibeau, T., 2013. Automatic Text Summarization: Past, Present and Future. In: Multi-source, Multilingual Information Extraction and Summarization. s.l.:Springer-Verlag Berlin Heidelberg, pp. 23-50. Steinberger, Josef, Jezek dan Karel, 2009. Evaluation Measures For Text Summarization. Computing and Informatics, Volume 28, pp. 1001-1026. Suanmali, L., Salim, N. dan Binwahlan, M. S., 2008. Automatic Text Summarization Using Feature Based Fuzzy Extraction. Jurnal Teknologi Maklumat Teknologi, 20(2), pp. 105-115. Tala, Z. F., 2004. A Study of Stemming Effects on Information Retriveal in Bahasa Indonesia, Amsterdam: Amsterdam University. Wajeed, M., Wajeed, A. dan Adilakshmi, T., 2014. Text Classification Using Machine Learning. Journal of Theoretical and Applied Information Technology, 10(2), pp. 2005-2009.  80  Witten, I. H. dan Frank, E., 2011. Data Mining Practical Machine Learning Tools and Techniques. Third ed. New York: Elsevier. Y. K, M., Jain, A. dan Gopalani, D., 2014. Survey on Graph and Cluster Based approaches in Multi-document Text Summarization. s.l., International Conference on Recent Advances and Innovations in Engineering. Yapinus, G., Erwin, A., Galinium, M. dan Muliady, W., 2014. Automatic MultiDocument Summarization for Indonesian Documents Using Hybrid Abstractive-Extractive Summarization Technique. Yogyakarta, 6th International Conference on Information Technology and Electrical Engineering.                        

SISTEM PERINGKASAN MULTI DOKUMEN BAHASA INDONESIA BERDASARKAN LDA-SIGNIFICANCE SENTENCES

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SISTEM PERINGKASAN MULTI DOKUMEN BAHASA INDONESIA BERDASARKAN LDA-SIGNIFICANCE SENTENCES

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