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Arya Wicaksana
Publications - 24
Citations - 92
Arya Wicaksana is an academic researcher. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 20 publications receiving 50 citations.
Papers
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Proceedings ArticleDOI
The Implementation of Winnowing Algorithm for Plagiarism Detection in Moodle-based E-learning
TL;DR: A plagiarism detection feature as a plug-in for the E-learning system that handles assignments that are in the format of text documents is proposed and successfully detects plagiarism on student assignment in E- learning UMN.
Journal ArticleDOI
Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches
TL;DR: In this paper , the authors used a multivariate prediction approach and three different recurrent neural networks (RNNs) namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTMs), and the gated recurrent unit (GRU).
Journal ArticleDOI
Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches
TL;DR: In this article , the authors used a multivariate prediction approach and three different recurrent neural networks (RNNs) namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTMs), and the gated recurrent unit (GRU).
Journal Article
Position Placement Dss Using Profile Matching And Analytical Hierarchy Process
TL;DR: This study uses the Profile Matching and Analytical Hierarchy Process (AHP) methods with competency aspects and weights set by the CHR Kompas Gramedia to design and build a decision support system for position placement.
Book ChapterDOI
Virtual Prototyping Platform for Multiprocessor System-on-Chip Hardware/Software Co-design and Co-verification
Arya Wicaksana,C. M. Tang +1 more
TL;DR: A virtual prototyping platform to address the ever-challenging multiprocessor system-on-chip (MPSoC) hardware/software co-design and co-verification requirements and is scalable up to but not limited to twelve processing elements and configurable to the extent of the OVPs generic memory models.