https://journal.ump.edu.my/daam <p><strong>DAAM</strong> is a biannually peer-reviewed journal (June and December Issues) - dedicated to publish significant advances covering data analytics and applied mathematics fields. DAAM welcomes submissions in statistics and data science, pure mathematics, operational research, applied mathematics, and computational mathematics. Publication in DAAM is free of charge.</p> <p><strong>Starting 2022, DAAM will be published as March and September Issues.</strong></p> Universiti Malaysia Pahang Al-Sultan AbdullahPublishing en-US Data Analytics and Applied Mathematics (DAAM) 2773-4854 https://journal.ump.edu.my/daam/article/view/10022 <p>Finite cyclic groups of p-power order, where p represents a prime number, have long been an interesting field of study in abstract algebra. This paper investigates the compatibility conditions that control their existence and behaviour. An overview of cyclic groups, automorphisms and their properties is given as groundwork for this research. By analysing the interaction between the group's order and its generator, we discovered the compatibility conditions and presented them as the primary finding in this paper.</p> Fatin Hanani Hasan Mohd Sham Mohamad Yuhani Yusof Nor Amirah Mohd Busul Aklan Copyright (c) 2023 The Author(s) https://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 1 7 10.15282/daam.v4i2.10022 https://journal.ump.edu.my/daam/article/view/10195 <p>Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.</p> Muhammad Haziq Abdul Hadi Nor Azuana Ramli Qamar UI Islam Copyright (c) 2023 The Author(s) https://creativecommons.org/licenses/by-nc/4.0 2023-09-30 2023-09-30 8 17 10.15282/daam.v4i2.10195