IDX30 Portfolio Construction using K-Means Clustering with MAD Risk Optimization and Sortino Ratio Evaluation

Rifki Pebriyandi (Department of Statistics, Tanjungpura University, Pontianak, Indonesia)
Evy Sulistianingsih (Department of Statistics, Tanjungpura University, Pontianak, Indonesia)
Hendra Perdana (Department of Statistics, Tanjungpura University, Pontianak, Indonesia)
Wirda Andani (Department of Statistics, Tanjungpura University, Pontianak, Indonesia)
Shantika Martha (Department of Statistics, Tanjungpura University, Pontianak, Indonesia)

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan

A stock portfolio plays an important role in managing risk and achieving optimal returns in volatile markets. This study proposes an integrated framework that combines K-Means Clustering, Mean Absolute Deviation (MAD), and the Sortino Ratio. The main contribution lies in linking clustering-based asset selection with downside risk optimization and evaluation, enabling portfolio construction that accounts for asset similarity, risk measurement, and investor-oriented performance assessment. This approach addresses the limitation of previous studies that apply these methods separately by providing a more structured basis for downside risk-adjusted portfolio selection. Using daily IDX30 stock data from June 2024 to June 2025, samples were selected based on index consistency. The results indicate that a portfolio of ANTM and INDF achieved the highest Sortino Ratio of 0.2179. These findings suggest that combining high-return stocks with moderate volatility, supported by clustering and downside risk optimization, can improve downside risk-adjusted performance, providing practical guidance for investors.