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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Christian, Yuriko
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Computer Science & IT

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Quantization-Based Novel Extraction Method Of EEG Signal For Classification Angreni, Ni Putu Dewi; Muliantara, Agus; Christian, Yuriko
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 2 (2020): JELIKU Volume 9 No 2, November 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v09.i02.p02

Abstract

In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, with the result being a feature vector that will be included in the artificial neural network classifier using the Keras library. The experiment carried out is to try to enter quantized and Non-quantized feature vectors into the classifier. As a result, the accuracy of the classification process with the quantization vector was 75%, and the accuracy in the Non-quantized vector classification process was only 58%. These results indicate the EEG signal quantization feature can represent the EEG signal object. Keywords: EEG signal, quantization, DEAP, feature extraction, pattern recognition
Specialty Coffee Cupping Score Prediction with General Regression Neural Network (GRNN) Christian, Yuriko; Darmawan, I Dewa Made Bayu Atmaja
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 9 No 2 (2020): JELIKU Volume 9 No 2, November 2020
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2020.v09.i02.p04

Abstract

Coffee is a plant that can be processed into beverages. The cupping score is a score for a coffee quality graded with an expert called Q grader. The cupping score will decide if a coffee may be called as specialty coffee. In this research, the cupping score will be predicted by the coffee properties and did not involve the Q grader for giving the score. The prediction of the score is obtained by using the GRNN method. The experiment consists of finding when the MAE and the MSE are converged and find the neuron's best number. The model's performance is measured with MSE and MAE with the best MSE value of 0.097 and MAE value 0.245.
Co-Authors Agus Muliantara Angreni, Ni Putu Dewi I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu