K-MEANS CLUSTERING OF INDONESIAN BANKING STOCKS USING FINANCIAL RATIOS
DOI:
https://doi.org/10.33480/dfsypr32Keywords:
Banking Issuers, Clustering, Data Mining, Financial Ratios, K-MeansAbstract
This study aims to classify banking sector issuers listed on the Indonesia Stock Exchange based on financial ratios, namely Return on Assets (ROA), Return on Equity (ROE), and Loan to Deposit Ratio (LDR), to assist investors in analyzing financial performance and making more objective investment decisions. The method used in this study is Knowledge Discovery in Databases (KDD) with the K-Means clustering algorithm. The dataset was obtained from the annual financial reports of 25 banking issuers for the period 2022–2024. The research stages consist of data selection, data cleaning, data transformation, data mining, and interpretation/evaluation. In the transformation stage, the average values of ROA, ROE, and LDR were calculated and normalized using the Min-Max method. The optimal number of clusters was determined using the Elbow method, while cluster quality was evaluated using the Silhouette Coefficient. The results indicate that the optimal number of clusters is three with a Silhouette Coefficient value of 0.5946. The clustering results consist of a dominant cluster containing 22 issuers with relatively stable financial performance, a second cluster consisting of two issuers characterized by higher lending activity and relatively higher risk, and a third cluster containing one issuer with significantly lower financial performance. These findings reveal latent patterns among banking issuers that may not be easily identified through conventional ratio analysis and provide a clearer structural overview of banking sector performance to support investment evaluation. These insights provide a clearer structural overview of the banking sector and may assist investors in identifying banks with comparable financial risk and performance profiles.
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