Klasifikasi Keminatan Menggunakan Algoritme Extreme Learning Machine dan Particle Swarm Optimization untuk Seleksi Fitur (Studi Kasus: Program Studi Teknik Informatika FILKOM UB) - Nur Afifah Sugianto; Imam Cholissodin; Agus Wahyu Widodo (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Majoring program in Informatics Engineering Program Faculty of Computer Science (FILKOM) Brawijaya University is a stabilization program for the profile of graduates of Informatics Engineering students so that each student has a special ability in accordance with the profile of graduates to be achieved. To be able to help the student in selecting the major program then a smart system is needed to determine the major program of each student that accordance with the interests and abilities of students. One methods of classification that can be used is Extreme Learning Machine (ELM) algorithm. However, the method does not have the ability to select features so it needs to be combined with Particle Swarm Optimization algorithm that can be used to perform feature selection automatically and optimally. This research uses 90 data of student study result with 25 features and 3 classes. Based on the research that has been done, the optimal parameters are the number of nodes in the hidden node is 20, the comparison of training data and testing data is 80%:20% (72 training data and 18 testing data), the number of particles is 120, the maximum iteration is 600 and the weight of inertia is 1. From these parameters, the system accuracy using ELM&PSO algorithm is 94.44% with 11 selected features. While the accuracy obtained from the ordinary ELM algorithm is only 66.67%. from the results of accuracy obtained, shows that the addition of PSO algorithm on ELM can improve the accuracy of common ELM algorithm.