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p-Index From 2020 - 2025
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All Journal ComEngApp : Computer Engineering and Applications Journal Sisforma: Journal of Information Systems
Wahyu Caesarendra
Universiti Brunei Darussalam
Computer Science & IT Education Engineering Library & Information Science

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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%.
Sentiment Analysis of Urban Opinions on COVID-19 Handling in Brunei Darussalam Using Lexicon Weighting and Machine Learning Algorithms Usman Ependi; Wahyu Caesarendra
SISFORMA Vol 11, No 1: May 2024
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v11i1.11957

Abstract

Crisis management of Covid-19 is closely related to how government provides policy measures and monitors the health conditions of residents and others. Residents will provide feedback (opinions) for any services provided by the government. The main issue in this area is understanding residents' opinions to become a source of information for sentiment in public policy. This study aims to analyze sentiment on crisis management of covid-19 in Brunei Darussalam. Lexicon weight and machine learning classifiers (random forest, k-nearest neighbors, naive Bayes, and decision trees) are used for handling this issue. The data used in this study comes from resident opinions on the BruHealth application, which is part of Brunei Darussalam Government Services. Based on the experimental results, the sentiment of crisis management of Covid-19 is positive. Lexicon weight is used as a basis for data labelling in machine learning classification. Classification results using random forest, k-nearest neighbors, naive Bayes, and decision trees get a significant accuracy of 83,8%, 73,7%, 55%, and 84,2%, respectively.
Co-Authors Mohamad Irfan Usman Ependi