Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Irfan Wahyudin, Taufik Djatna, Wisnu A Kusuma

Judul : Cluster analysis for SME risk analysis documents based on Pillar K-Means

Abstrak :
In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal.

Tahun : 2018 Media Publikasi : Jurnal Internasional

Kategori : Jurnal No/Vol/Tahun : 14 / 2 / 2016

ISSN/ISBN : 1693-6930,

PTN/S : IPB Bogor Program Studi : ILMU KOMPUTER

Bibliography :
[1] Soares J, Pina J, Ribeiro M, Catalao-Lopes M. Quantitative vs. Qualitative Criteria for Credit Risk Assessment. Frontiers in Finance and Economics. 2011; 8(1): 68-87. [2] Chih FT, Jhen WW. Using neural network ensembles for bankruptcy prediction and credit scoring. Experts System with Applications. 2008; 34: 2639-2649. [3] Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. 2014: 1093-1113. [4] Xu K, Shaoyi S, Li J, Yuxia S. Mining comparative opinions from customer reviews for Competitive Intelligence. Decision Support Systems. 2011; 50: 743-754. [5] Ghiassi M, Skinner J, Zimbira D. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications. 2013; 40(16): 6266- 6282. [6] Li N, Wu DD. Using text mining and sentiment analysis for online forums hotspot detection. Decision Support Systems. 2010; 48: 354-368. [7] Xu H, Zhai Z, Liu B, Jia P. Clustering Product Features for Opinion Mining. Proceedings of the fourth ACM international conference on Web search and data mining. ACM. 2011: 347-354. [8] Zhang D, Dong Y. Semantic, Hierarchical, Online Clustering of Web Search Results. Advanced Web Technologies and Applications. 2004; 30(07): 69-78. [9] Esuli, Sebastiani. Sentiwordnet: A publicly. International Conference on Language Resources and Evaluation (LREC). 2006; 1: 417-422. [10] Thelwall M. Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology. 2010; 1: 2544-2558. [11] Gonçalves P, Benevenuto F, Araujo M, Cha M. Comparing and Combining Sentiment Analysis Methods. Conference on Online Social Networks (COSN). 2013; 1: 27-38. [12] Denecke K. Using SentiWordNet for Multilingual Sentiment Analysis. International Council for Open and Distance Education Conference. 2008. [13] Lunando E, Purwarianti A. Indonesian Social Media Sentiment Analysis with Sarcasm Detection. Advanced Computer Science and Information Systems (ICACSIS). 2013; 1: 195-198. [14] Zhang Q, Liu F, Xie B, Huang Y. Index Selection Preference and Weighting for Uncertain Network Sentiment Emergency. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(1): 287- 295. [15] Barakbah AR, Kiyoki Y. A Pillar Algorithm for K-Means Optimization by Distance Maximization for Initial Centroid Designation. IEEE Symposium on Computational Intelligence and Data Mining (CIDM). 2009; 1: 61-68. [16] Manning CD, Prabakhar R, Schütze H. An Introduction of Information Retrieval. Cambridge: Cambridge University Press. 2009: 118-119. [17] Zhang PY. A HowNet-based Semantic Relatedness Kernel for Text Classification. TELKOMNIKA. 2013; 11(4): 1909-1915. [18] [18] Osinski SL, Stefanowski J, Weiss D. Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition. Master Thesis. Poznan: Poznan University of Technology. 2003. [19] Rousseeuw PJ. Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics. 1986; 20: 53-65.

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Detail Karya Ilmiah Dosen

Irfan Wahyudin, Taufik Djatna, Wisnu A Kusuma

Judul	:	Cluster analysis for SME risk analysis documents based on Pillar K-Means
Abstrak	:	In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal.
Tahun	:	2018	Media Publikasi	:	Jurnal Internasional
Kategori	:	Jurnal	No/Vol/Tahun	:	14 / 2 / 2016
ISSN/ISBN	:	1693-6930,
PTN/S	:	IPB Bogor	Program Studi	:	ILMU KOMPUTER
Bibliography	:	[1] Soares J, Pina J, Ribeiro M, Catalao-Lopes M. Quantitative vs. Qualitative Criteria for Credit Risk Assessment. Frontiers in Finance and Economics. 2011; 8(1): 68-87. [2] Chih FT, Jhen WW. Using neural network ensembles for bankruptcy prediction and credit scoring. Experts System with Applications. 2008; 34: 2639-2649. [3] Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal. 2014: 1093-1113. [4] Xu K, Shaoyi S, Li J, Yuxia S. Mining comparative opinions from customer reviews for Competitive Intelligence. Decision Support Systems. 2011; 50: 743-754. [5] Ghiassi M, Skinner J, Zimbira D. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications. 2013; 40(16): 6266- 6282. [6] Li N, Wu DD. Using text mining and sentiment analysis for online forums hotspot detection. Decision Support Systems. 2010; 48: 354-368. [7] Xu H, Zhai Z, Liu B, Jia P. Clustering Product Features for Opinion Mining. Proceedings of the fourth ACM international conference on Web search and data mining. ACM. 2011: 347-354. [8] Zhang D, Dong Y. Semantic, Hierarchical, Online Clustering of Web Search Results. Advanced Web Technologies and Applications. 2004; 30(07): 69-78. [9] Esuli, Sebastiani. Sentiwordnet: A publicly. International Conference on Language Resources and Evaluation (LREC). 2006; 1: 417-422. [10] Thelwall M. Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology. 2010; 1: 2544-2558. [11] Gonçalves P, Benevenuto F, Araujo M, Cha M. Comparing and Combining Sentiment Analysis Methods. Conference on Online Social Networks (COSN). 2013; 1: 27-38. [12] Denecke K. Using SentiWordNet for Multilingual Sentiment Analysis. International Council for Open and Distance Education Conference. 2008. [13] Lunando E, Purwarianti A. Indonesian Social Media Sentiment Analysis with Sarcasm Detection. Advanced Computer Science and Information Systems (ICACSIS). 2013; 1: 195-198. [14] Zhang Q, Liu F, Xie B, Huang Y. Index Selection Preference and Weighting for Uncertain Network Sentiment Emergency. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(1): 287- 295. [15] Barakbah AR, Kiyoki Y. A Pillar Algorithm for K-Means Optimization by Distance Maximization for Initial Centroid Designation. IEEE Symposium on Computational Intelligence and Data Mining (CIDM). 2009; 1: 61-68. [16] Manning CD, Prabakhar R, Schütze H. An Introduction of Information Retrieval. Cambridge: Cambridge University Press. 2009: 118-119. [17] Zhang PY. A HowNet-based Semantic Relatedness Kernel for Text Classification. TELKOMNIKA. 2013; 11(4): 1909-1915. [18] [18] Osinski SL, Stefanowski J, Weiss D. Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition. Master Thesis. Poznan: Poznan University of Technology. 2003. [19] Rousseeuw PJ. Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. Computational and Applied Mathematics. 1986; 20: 53-65.
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