PARTICLE SWARM OPTIMIZATION SEBAGAI PENENTU NILAI BOBOT PADA ARTIFICIAL NEURAL NETWORK BERBASIS BACKPROPAGATION UNTUK PREDIKSI TINGKAT PENJUALAN MINYAK PELUMAS PERTAMINA - Muhammad Ali Ridla (Jurnal Ilmiah Informatika, 2018)
The lubricating oil industry is one part of the oil and gas sector which is still one of the main pillars of economic growth in Indonesia. Sales predictions are needed by companies and policy makers as planning materials and economic development strategies to increase income in the future. Predictions that have a better level of accuracy can provide appropriate decisions. Various methods have been used, the Artificial Neural Network algorithm is one of the most widely used, especially in the Backpropagation (BPNN) structure which can predict non linear time series data. Backpropagation has been proven to have a better level of accuracy compared to econometric methods such as ARIMA. The integration of Backpropagation algorithm with other algorithms needs to be done to overcome the shortcomings and improve the ability of the National Land Agency itself. Particle Swarm Optimization (PSO) which is used as an optimization determinant of attribute weight values in the network structure of BPNN shows good results. After testing, BPNN without PSO has a Squared Error (SE) level of 0.012 and a Root Mean Aquared Error (RMSE) of 0.111. While BPNN with PSO has SE levels of 0.004 and RMSE of 0.059. This shows that there is a significant decrease in the error rate after the PSO algorithm is added to the BPNN structure which is 46.85%.