Perbandingan Metode K-Means dan Metode DBSCAN pada Pengelompokan Saham Indeks LQ45 - Iqbal Ali Mansyah Wardana; Finda Nur Lailiyah; Noviyanti Santoso (Jurnal Sains dan Informatika, 2026)
The increase in public interest in investment, especially among young people, has caused a surge in the number of investors in Indonesia since 2019. This condition is accompanied by the increasing number of companies listed on the Indonesia Stock Exchange (BEI), especially on the LQ45 stock index which is one of the main references for investors. . However, stock market dynamics often make it difficult for investors to choose shares with optimal financial performance. Financial ratio analysis of financial reports is an important tool for assessing company performance. However, financial ratio data is numerical and has not been clearly grouped, so a grouping method is needed to provide a better understanding of company performance patterns. This research compares two clustering methods, namely K-Means and DBSCAN, in grouping shares in the LQ45 index based on financial ratios such as Return on Equity (ROE), Net Profit Margin (NPM), Debt to Equity Ratio (DER), Debt to Asset Ratio (DAR), Company Size (UP), and Stock Trading Volume from the period February to July 2023. K-Means is known to be effective for clearly distributed data, while DBSCAN excels in handling data with complex distribution forms and the existence of outliers. The results of this research indicate that the DBSCAN method is better in grouping companies because it has higher Silhouette Score and Dunn Index values ​​compared to the K-Means method.