PREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA DENGAN METODE NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION - Mohammad Badrul (Lembaga Penelitian dan Pengabdian Pada Masyarakat, 2013)
General elections are a means of implementation of the sovereignty of the people in the unitary state of Indonesia based on Pancasila and 1945 Constitution. Elections held in Indonesia is to choose the leadership of both the president and vice president, member of parliament, parliament, and the DPD. The related research of general election usually using decision tree algorithm neural network algorithm. Each of the methods has strengths and weakness, but a neural network algorithm can solve a problem in the decision tree algorithm. The accuracy using a neural network algorithm in predicting the election has less accurate. In this study created a model neural network algorithm and neural network algorithm model based genetic algorithm to get the rule in predicting the outcome of legislative elections and provide a more accurate value of accuracy. After testing the two models namely neural network algorithm and neural network algorithm based on particle swarm optimization, the results obtained are the neural network algorithm produces an accuracy value by 98,50% and the AUC value of 0.982, but after the addition of neural network algorithm based on particle swarm optimization value of 98,85 % accuracy and AUC value of 0.996. So both methods have an accuracy rate of 0.35 % difference and the difference in the AUC of 0.14.