PEMBENTUKAN PORTOFOLIO OPTIMAL SAHAM IDXENERGY DENGAN PARTICLE SWARM OPTIMIZATION DAN GENETIC ALGORITHM - Talalu, Lidya Ananda; Azizah, Syasya Qonita (Prodi Pendidikan Ekonomi Universitas Balikpapan, 2026)
This study aims to compare the performance of an optimized portfolio of energy sector stocks included in the IDXENERGY index under market volatility conditions resulting from the conflict in Iran. The methods used include Partitioning Around Medoids (PAM) for stock clustering, as well as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for determining the optimal portfolio weights. The data used consists of the daily stock prices of energy sector companies listed on the Indonesia Stock Exchange during the observation period. Portfolio performance is evaluated using return, risk, and Sharpe ratio metrics. The results of the study show that PSO is capable of delivering better performance, with a Sharpe Ratio of 3.5634, even though GA yields a higher return. These findings indicate that PSO generates portfolios with a more controlled risk-return ratio under volatile market conditions caused by geopolitical uncertainty.