Open AccessBanking Clustering Study Based On Fuzzy C-mean and Fuzzy Gustafson Kessel
Author(s) -
Kartika Ayu Kinanti,
Hari Sukarno,
Elok Sri Utami
Publication year - 2021
Publication title -
international journal of environmental, sustainability and social science
Language(s) - English
Resource type - Journals
eISSN - 2721-0871
pISSN - 2720-9644
DOI - 10.38142/ijesss.v2i1.58
Subject(s) - cluster analysis , soundness , fuzzy logic , competition (biology) , cluster (spacecraft) , matlab , fuzzy clustering , computer science , actuarial science , business , artificial intelligence , ecology , biology , programming language , operating system
The banking sector as one of the economic drivers plays an important role in society. Over time, bank operations did not only raise funds from the public but were more complex. The development of the banking industry can be seen from the number of banks in Indonesia that have spurred the level of competition. Of course, the bank must pay attention to its health. The use of bank soundness level parameters or RGEC combined with clusters is interesting to study. By using the cluster method, banks can be classified based on the parameters of their health level. This study aims to analyze the RGEC-based bank grouping classification generated by the Fuzzy C-Means and Fuzzy Gustafson Kessel clustering analysis using financial ratio data on 80 conventional banks in Indonesia. The software used in this study is Matlab r2015b. The results showed that the FCM clustering had a smaller standard deviation than FGK so that the first cluster in the FCM showed that the banks were in good condition compared to the other clusters even though the overall condition of banks in Indonesia was good when viewed from their financial performance.