Penerapan Metode GA-TOPSIS untuk Sistem Seleksi Karakter Game dengan Pembobotan Dinamis Berbasis Sensor Suhu - Prakasa, Aji Bagas; Nugroho, Fresy; Faisal, Muhammad; Lestari, Tri Mukti; N, Alfina Nurrahma; Taufiqulhakim, Adnan Muhammad (TIN: Terapan Informatika Nusantara, 2025)
This study aims to develop a decision support system for optimal character selection by implementing a hybrid Genetic Algorithm and TOPSIS (GA-TOPSIS) method that considers temporal variations in criterion weighting. The approach combines the optimization capability of Genetic Algorithms for automatic weight determination with the multi-criteria decision-making technique of TOPSIS. The research results demonstrate that GA optimization produces significant variations in weighting according to time scenarios: morning conditions dominated by Movement (82%), daytime emphasizing Height (52%) and Health (38%), and nighttime dominated by Defense (85%).Evaluation using TOPSIS yields different alternative rankings for each scenario. In morning conditions, alternative A4 achieves the highest score (0.83) due to its superiority in Movement criteria. The daytime scenario ranks A2 as optimal ( =0.90) because of its performance in Height and Health, while at night, A3 excels ( =0.89) with the best Defense. Result consistency is shown by A1 consistently ranking lowest due to minimal criterion values. This research makes important contributions to the development of adaptive decision support systems, particularly those requiring dynamic weight adjustments based on environmental changes. The potential integration with IoT technology for real-time weight updates adds value to the method's application.