Classification of interests and talents in early adult phase based on RMIB test with neural network

Abstract

The brain in the human body is responsible for regulating the overall work of the human body and mind. The left part of the brain is the center of intelligence or commonly called Intelligence Quotient (IQ).  Intelligence can come from genes received by children from their parents that will continue to develop along with a person's maturity process.  An individual will go through a transition period or transition period, namely in the early adult phase so that individuals in the early adult phase often experience unstable psychic conditions. This labile condition occurs in early adult individuals in this case, namely students who are still not sure what potential interests and talents they have, causing students It felt wrong to take the major. The purpose of this study is to classify interests and aptitudes from EEG data obtained from interviewees with RMIB test stimulus. In this study, testing will be carried out on the object of study where the   object of study is an individual in the early adult phase with an age range between 18-30 years. The test is carried out using a beta signal (12-30 Hz) resulting from an Electroencephalogram (EEG) signal filter generated from recording EEG data with the NeuroSky Mindwave tool and then reduced to get the best value or component with the Principal Component Analysis (PCA) method.  EEG data recording is carried out 3 times with data recording intervals every 14 days. EEG data is   information that we can get from activity waves in the brain, because waves in the brain cannot be observed visually. Testing on this study.  The EEG data obtained will go through the pre-processing stage, namely signal filters and signal reduction   and then will be classified using neural networks with a backpropagation algorithm with Using 1 layer of hidden layer. In this study, the results of the RMIB test carried out by the interviewees were calculated by psychologists (expert judgement) which were used as comparison data or the output produced by the system.   Testing is carried out by cross validation, which is to cross-test each data retrieval. Accuracy testing on the first fetch resulted in an accuracy of 92.8571%, in the second data retrieval it produced an accuracy of 78.571%, in the third data retrieval it resulted in an accuracy of 71.4285% with an average accuracy produced by the system of 80.9523%.