Prediksi Luas Serangan Hama pada Tanaman Padi Menggunakan Metode Extreme Learning Machine (ELM) dan Particle Swarm Optimization (PSO) - Cornelius Bagus Purnama Putra; Randy Cahya Wihandika; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2020)
Indonesia is one of the countries with the largest population in the world. The majority of the population consuming rice as the staple food, rice becomes an important commodity. Recent global warming has resulted in extreme climate change, so that it can affect crop productivity and the intensity of OPT (Plant Pests) attack on rice plants. In meeting the increasing need for rice, it is necessary to prevent pest attack so that widespread prediction of pest attack area is needed in order to know earlier about upcoming pest attack. This study used hybrid algorithm Extreme Learning Machine and Particle Swarm Optimization with used data on pest attacks and climatology of Sidoarjo Regency from January 2009 to December 2018. Based on the research, the optimal parameters obtained are the ratio of training data 80% and testing data 20%, activation function of TanH, total population of 40, combination acceleration coefficient of 1 & 2, inertia weight limit of 0,4 & 0,9, hidden neuron of 5, and a maximum iteration of 100. Based on these parameters, the average value of the Mean Absolute Percentage Error (MAPE) is 25.143% which is included in the MAPE category of quite good, which is within the range of 20% -50%.