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All Journal JOIV : International Journal on Informatics Visualization IJISTECH (International Journal Of Information System & Technology) IJISTECH
Andi Nur Rachman
Universitas Siliwangi Tasikmalaya, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Comparison of the Multinomial Naive Bayes Algorithm and Decision Tree with the Application of AdaBoost in Sentiment Analysis Reviews PeduliLindungi Application Cecep Muhamad Sidik Ramdani; Andi Nur Rachman; Rizki Setiawan
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.257

Abstract

One solution that the Indonesian government has implemented in controlling and tracking COVID-19 cases is using the PeduliLindungi application. User review data on the PeduliLindungi application is available on the Google Play Store, the data can be analyzed to determine the trend of public sentiment towards the PeduliLindungi application using sentiment analysis techniques. One of the methods used for sentiment analysis is machine learning, but in the machine learning method there is a problem, namely the relatively low level of accuracy. In this study, there are 2 machine learning algorithms that are used and compared, namely the Multinomial Naïve Bayes (MNB) and Decision Tree (DT) algorithms combined with the AdaBoost (AB) method to improve the accuracy of the PeduliLindungi application review data classification accuracy. In the experiment conducted, the tendency of public sentiment towards the PeduliLindungi application was 67% positive and 33% negative from a total of 8305 data. Multinomial Naïve Bayes before being combined with AdaBoost produces an average accuracy value of 83,7%, while Decision Tree produces an average accuracy value of 82,8%. After being combined, MNB+AB produces an average accuracy value of 88,8%, while the DT+AB method produces an average accuracy value of 84,1%. The use of AdaBoost can improve the accuracy of the Multinomial Naive Bayes algorithm and Decision Tree for the PeduliLindungi application review data classification process.
Comparison of the Multinomial Naive Bayes Algorithm and Decision Tree with the Application of AdaBoost in Sentiment Analysis Reviews PeduliLindungi Application Cecep Muhamad Sidik Ramdani; Andi Nur Rachman; Rizki Setiawan
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.257

Abstract

One solution that the Indonesian government has implemented in controlling and tracking COVID-19 cases is using the PeduliLindungi application. User review data on the PeduliLindungi application is available on the Google Play Store, the data can be analyzed to determine the trend of public sentiment towards the PeduliLindungi application using sentiment analysis techniques. One of the methods used for sentiment analysis is machine learning, but in the machine learning method there is a problem, namely the relatively low level of accuracy. In this study, there are 2 machine learning algorithms that are used and compared, namely the Multinomial Naïve Bayes (MNB) and Decision Tree (DT) algorithms combined with the AdaBoost (AB) method to improve the accuracy of the PeduliLindungi application review data classification accuracy. In the experiment conducted, the tendency of public sentiment towards the PeduliLindungi application was 67% positive and 33% negative from a total of 8305 data. Multinomial Naïve Bayes before being combined with AdaBoost produces an average accuracy value of 83,7%, while Decision Tree produces an average accuracy value of 82,8%. After being combined, MNB+AB produces an average accuracy value of 88,8%, while the DT+AB method produces an average accuracy value of 84,1%. The use of AdaBoost can improve the accuracy of the Multinomial Naive Bayes algorithm and Decision Tree for the PeduliLindungi application review data classification process.
Implementation of Convolutional Neural Network and Long Short-Term Memory Algorithms in Human Activity Recognition Based on Visual Processing Video Andi Nur Rachman; Husni Mubarok; Euis Nur Fitriani Dewi; Rama Edwinda Putra
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1504

Abstract

Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.
Co-Authors Cecep Muhamad Sidik Ramdani Euis Nur Fitriani Dewi Husni Mubarok Rama Edwinda Putra Rizki Setiawan