TELKOMNIKA Telecommunication, Computing, Electronics and Control
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer
Subject
Accelerometer, Earthquakes, Human activity, K-NN algorithm, Signal recognition
Description
Micro-electro-mechanical-system accelerometer is able to detect acceleration signal caused by earthquake. Such type of accelerometer is also used by smartphones. There are few algorithms that can be used to recognize the type of acceleration signal from smartphone. This study aims to find signal recognition algorithm in order to consider the most proper algorithm for earthquake signal detection. The initial stage of designing the recognizer is data collection for each type of signal classification. The next step is to apply a highpass filter to separate the signals collected from the gravitational acceleration signal. The signal is divided into several segments. The system will extract features of each signal segment in the time and frequency domain. Each signal segment is then classified according to the type of signal using the classifier through a series of training data processes. The classifier which has the highest accuracy value is exported into the new input signal modeling. As the result, fine K-NN algorithm has the highest level of accuracy in the classification. The fine K-NN algorithm has an accuracy rate of 99.75% in
the classification of human activity signals and earthquake signals with a
memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
the classification of human activity signals and earthquake signals with a
memory capacity of 6,044 kilobytes and processing time of 15.93 seconds. This algorithm has the best classifier criteria compared to decision tree, support vector machine and linear discriminant analysis algorithms.
Creator
Hapsoro Agung Nugroho, Haryas Subyantara Wicaksana, Hariyanto, Rista H. Virgianto
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14708
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Hapsoro Agung Nugroho, Haryas Subyantara Wicaksana, Hariyanto, Rista H. Virgianto, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4075.
Algorithm performance comparison for earthquake signal recognition on smartphone’s accelerometer,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4075.