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All Journal JUITA : Jurnal Informatika Indonesian Journal of Electrical Engineering and Computer Science
Asnor Juraiza Ishak
Universiti Putra Malaysia
Computer Science & IT

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Development of fall detection and activity recognition using threshold based method and neural network Sai Siong Jun; Hafiz Rashidi Ramli; Azura Che Soh; Noor Ain Kamsani; Raja Kamil Raja Ahmad; Siti Anom Ahmad; Asnor Juraiza Ishak
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1338-1347

Abstract

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football Coach Slamet Riyadi; Muhammad Dzaki Mubarok; Cahya Damarjati; Asnor Juraiza Ishak
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21847

Abstract

Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data.
Co-Authors Azura Che Soh Cahya Damarjati Hafiz Rashidi Ramli Muhammad Dzaki Mubarok Noor Ain Kamsani Raja Kamil Raja Ahmad Sai Siong Jun Siti Anom Ahmad Slamet Riyadi