PENINGKATAN OPTIMASI SENTIMEN DALAM PELAKSANAAN PROSES PEMILIHAN PRESIDEN BERDASARKAN OPINI PUBLIK DENGAN MENGGUNAKAN ALGORITMA NAÃVE BAYES DAN PARICLE SWARM OPTIMIZATION - Betesda Betesda (Fakultas Ilmu Komputer dan Desain - Unsurya, 2020)
Abstract- The development of increasingly advanced IT in the process of presidential elections. When the Presidential election of 2014 yesterday has a lot of people use the phrase does not educate inappropriate to be delivered among the public. Pros and cons indeed occur among people are so warm that they pour on the internet. This happens because when getting warm diperbincangan 2014 presidential election yesterday happened pengkubu-kubuan two candidates. Society can not adjust the development of IT process well. Naive Bayes is widely used for classification problems in data mining and machine learning for its simplicity and accuracy of classification impressive. Naive Bayes classifier has been shown to be very effective to solve the problem of large scale for text categorization with high accuracy. In addition to having many capabilities mentioned above, however this method has a drawback in the assumptions that are difficult to fulfill, namely the independence of the feature. Particle Swarm Optimization (PSO) is an evolutionary computation technique which is able to produce globally optimal solution in the search space through the interaction of individuals in a swarm of particles. PSO is widely used to solve optimization problems as well as the feature selection. Accuracy is generated on Naive Bayes algorithm amounted to 63.85% and AUC by 0523, while Naive Bayes and Particle Swarm Optimmization with an accuracy of 71.15% and the AUC of 0.600. It can be concluded that the application of optimization can improve the accuracy of 63.85% to 71.15%. Naive Bayes Model and Particle Swarm Optimization can provide solutions to the problems of classification review of public opinion news of the election in order to more accurately and optimally.Â Keywords:Public Opinion, Classification, Naive Bayes, Particle Swarm Optimization, Text Mining.