F
Firdaus Firdaus
Researcher at Sriwijaya University
Publications - 192
Citations - 679
Firdaus Firdaus is an academic researcher from Sriwijaya University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 8, co-authored 59 publications receiving 345 citations. Previous affiliations of Firdaus Firdaus include Universiti Teknologi Malaysia & State University of Malang.
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Developing Critical Thinking Skills of Students in Mathematics Learning
TL;DR: In this paper, the effects of mathematical learning modules based on problem-based learning to critical thinking skills at secondary school students in District of Bone, South Sulawesi, Indonesia were identified.
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Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification
Siti Nurmaini,Annisa Darmawahyuni,Akhmad Noviar Sakti Mukti,Muhammad Naufal Rachmatullah,Firdaus Firdaus,Bambang Tutuko +5 more
TL;DR: This research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL and demonstrates, the proposed DL model can extract high-level features not only from the training data but also from unseen data.
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An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique
Siti Nurmaini,Radiyati Umi Partan,Wahyu Caesarendra,Tresna Dewi,Muhammad Naufal Rahmatullah,Annisa Darmawahyuni,Vicko Bhayyu,Firdaus Firdaus +7 more
TL;DR: The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.
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Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks
Siti Nurmaini,Alexander Edo Tondas,Annisa Darmawahyuni,Muhammad Naufal Rachmatullah,Radiyati Umi Partan,Firdaus Firdaus,Bambang Tutuko,Ferlita Pratiwi,Andre Herviant Juliano,Rahmi Khoirani +9 more
TL;DR: A simple algorithm of a discrete wavelet transform coupled with one-dimensional convolutional neural networks (1D-CNNs) to classify three classes: Normal Sinus Rhythm, AF and non-AF (NAF) is proposed that can aid AF diagnosis in clinics and patient self-monitoring to improve early detection and effective treatment of AF.
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Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments
TL;DR: This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments.