Peramalan Pemakaian Air Pada PLTGU Di Pembangkitan Listrik Jawa Bali Unit Gresik Menggunakan Extreme Learning Machine Dengan Optimasi Algoritme Genetika - Heny Dwi Jayanti; Imam Cholissodin; Edy Santoso (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Water is an absolute necessity every day that has an important role. One of the utilization of seawater used for the industrial sector is PLTGU in PT Pembangkitan Jawa Bali. This makes the electricity industry has treated the sea water into fresh water called desalination process. However, in the PLTGU process often experience problems in water treatment such as the occurrence of leaking pipe due to corrosion, the difference of water filling treatment, and the long-time desalination process resulted in unstable turbine performance. With some problems that arise, then needed a solution. In this study, researchers have proposed a water forecasting system using the method of extreme learning machine (ELM) with the optimization of Genetic Algorithm. The genetic algorithm is used to optimize the input weight values obtained randomly on the ELM method. Meanwhile, to represent chromosomes using real code. At the reproduction stage using extended intermediate crossover method and random mutation method. The result of ELM test method and genetic algorithm resulted in average MAPE value of 0.428 with a parameter value of crossover rate (Cr) value 0.4 and mutation rate (Mr) equal to 0.6, popsize amount 200, number of generation 1000, and training data amount 80% of the entire dataset. From the results obtained MAPE, shows that the combined ELM method with genetic algorithm able to minimize the error value in forecasting compared with the ELM method.