Lala Septem Riza, Muhammad Naufal Fazanadi, Judhistira Aria Utama, Taufiq Hidayat, Khyrina Airin Fariza Abu Samah
Motif discovery has emerged as one of the most useful techniques in processing time-series data. One of the implementations of motif discovery is in case study 1:1 mean motion resonance (MMR) in the astronomy field. This study aims to build a computational model and its implementation to process time-series data and predict 1:1 MMR from asteroid orbital elements in time-series form. This model proposes Symbolic Aggregate approximation (SAX) and Random Projection (RP) algorithms implemented in the Python programming language. Some experiments involving ten asteroids’ orbital elements data have been carried out to validate the program. From the results obtained, we conclude that our computational model can predict the location of the motif and with which planet the motif is found for 1:1 resonance to occur. © 2021, Semnan University, Center of Excellence in Nonlinear Analysis and Applications. All rights reserved.
Department of Computer Science Education, Faculty of Mathematics and Natural Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia; Department of Physics Education, Faculty of Mathematics and Natural Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia; Astronomy Research Division, Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Bandung, Indonesia; Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Cawangan Melaka Kampus Jasin, Melaka, Malaysia
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