DETEKSI EJAAN BERDASARKAN SINYAL P300 DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

MUHARROM, KGS. M. RUSDIANSYAH and Primartha, Rifkie (2021) DETEKSI EJAAN BERDASARKAN SINYAL P300 DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Brain Computer Interface (BCI) is a system that connects the human brain with the outside world for people who have motor skills disability problems. One form of utilization is the P300 speller which is used for character recognition or detection by classifying the P300 signal. The Convolutional Neural Network (CNN) method is a deep learning method that can be used to handle signal problems with ID-CNN. At the initial stage the data signal will be transformed and followed by a duplication process using RandomOverSampling because the amount of data in each class is not balanced. The data will be divided into training, validation, and test data. After that, a training with CNN will be conducted and followed by an evaluation to find the best model. The test results from this study are a good-fitting CNN model with an evaluation value consisting of an accuracy of 94.27%, precision of 90.64%, sensitivity / recall of 98.30%, and f-measure of 94.31%. Based on the test, the CNN method can be used and implemented in authentication detection based on the P300 signal.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Deteksi Ejaan, Sinyal P300, Convolutional Neural Network, Deep Learning
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	KGS. M. RUSDIANSYAH MUHARROM
Date Deposited:	18 Jan 2022 05:14
Last Modified:	18 Jan 2022 05:14
URI:	http://repository.unsri.ac.id/id/eprint/61729

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