Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means
| Main Authors: | Wahyudin, Irfan; Bogor Agricultural University, Djatna, Taufik; Bogor Agricultural University, Kusuma, Wisnu Ananta; Bogor Agricultural University |
|---|---|
| Other Authors: | Irfan Wahyudin, Bogor Agricultural University, Department of Computer Science, Taufik Djatna, Bogor Agricultural University, Department of AgroIndustrial-Technology, Wisnu Ananta Kusuma, Bogor Agricultural University, Department of Computer Science |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2016
|
| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2385 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2385/2350 |
Daftar Isi:
- 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.