SEGMENTATION OF THE FINANCIAL PERFORMANCE OF PUBLIC COMPANIES ON THE INDONESIA STOCK EXCHANGE USING THE K-MEANS CLUSTERING METHOD
DOI:
https://doi.org/10.36526/sosioedukasi.v15i2.8506Keywords:
K-Means Clustering, Data Mining, Financial Performance, Indonesia Stock Exchange, Issuer SegmentationAbstract
This study applies data mining techniques, particularly K-Means Clustering, to segment the financial performance of publicly listed companies on the Indonesia Stock Exchange (IDX). Secondary data covering 967 issuers with four key financial variables including Current Ratio, EBIT Margin, Leverage Ratio, and Debt-to-Equity Ratio were sourced from the FY2024 financial database. After data cleaning and outlier removal using Z-score method, 650 valid observations were obtained. Optimal cluster determination using the Elbow and Silhouette methods yielded k=4 as the optimal value with a Silhouette Score of 0.4527. The analysis groups issuers into four clusters: (1) Liquid-Conservative Cluster (n=54) with very high current ratio and low leverage, (2) Moderate-Healthy Cluster (n=454) with balanced financial profile, (3) High-Leverage Cluster (n=131) with dominant debt ratios, and (4) Distress Cluster (n=11) with highly negative EBIT Margin. ANOVA tests confirm significant differences across clusters on all variables (p<0.001). These findings provide practical implications for investors in portfolio diversification strategies and for regulators in monitoring systemic risk in Indonesia's capital market.
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