Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means
Abstract: 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.
Keywords: risk analysis, SME
business, centroid optimazion, K-Means, opinion mining, pillar algorithm, sentiment
analysis
Author: Irfan Wahyudin
Journal Code: jptkomputergg160184
