Penerapan Data Mining pada Suku Bunga Investasi Deposito di Indonesia Menggunakan Metode K-Means Clustering untuk Pengelompokan Profitabilitas

Abstract

Investment in Indonesia has several types, one of which is deposits through banks, namely individual or collective customers lending excess funds to banks and channeling them in the form of credit. This research is about clustering on several Banks, those are Regional, Private, Foreign, Persero and General Banks which have different interest rates for investments. The method used in this study is the Knowledge Discovery using Database (KDD) method, using RapidMiner tools. The algorithm used to perform clustering is the K-Means algorithm. The data that was used is data on investment credit interest rates from several banks in Indonesia obtained from BPS (Central Statistics Agency) Indonesia. This data is taken from 2009-2020. The clustering results obtained are 3 clusters, where cluster 1 is Regional Bank, which turns out to have stable and high loan interest rates, then cluster 2 is Foreign Bank, which also has the lowest and unstable interest rate. While cluster 3 is a private bank and a state-owned company, these two banks have similar interest rates and levels of stability. In addition, these two banks are in the middle for investment options.

References

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