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All Journal ComEngApp : Computer Engineering and Applications Journal Knowledge Engineering and Data Science
Wahyu Caesarendra
Universiti Brunei Darussalam
Computer Science & IT Engineering

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Classification Method of Hand Gestures Based on Support Vector Machine Wahyu Caesarendra; Mohamad Irfan
Computer Engineering and Applications Journal Vol 7 No 3 (2018)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (630.599 KB) | DOI: 10.18495/comengapp.v7i3.269

Abstract

This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis (PCA) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%.
EEG Classification while Listening to Murottal Al-Quran and Classical Music using Random Forest Method Heni Sumarti; Fahira Septiani; Agus Sudarmanto; Wahyu Caesarendra; Rizki Edmi Edison
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p157-169

Abstract

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music.  The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification.  The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal al-Quran stimulation were 45.32% delta, 31.60% alpha, 17.02 theta and 6.05% beta. Meanwhile, the average brain waves of teenagers when given classical music stimulation were 46.54% delta, 28.64% alpha, 19.21% theta and 5.50% beta. Classification is obtained with the best value that frequently appears (mode) from the prediction results for each sample using random forest methods. The accuracy, precision, and recall of classifying adolescent brain waves when given murottal and classical music stimuli using the Random Forest method with cross-validation technique (optimum at k-fold=5) were 65.38%, 76.92%, and 70.00%, respectively.  The results of this study show that stimulation using murottal al-Quran and classical music effectively improves adolescent relaxation conditions.
Co-Authors Agus Sudarmanto Fahira Septiani Heni Sumarti Mohamad Irfan Rizki Edmi Edison