@inproceedings{ca8a43233be1427ea8705b46dea7d37a,
title = "Can smartphones be used to detect an earthquake? Using a machine learning approach to identify an earthquake event",
abstract = "The possibility of using smart phone accelerometer to detect earthquake is investigated in this research. Experiments are designed to learn the pattern of an earthquake signal recorded from smart phone's accelerometer. The signal is processed using N-gram modeling as feature extractor for machine learning. For the classifier, this study use Na{\"i}ve Bayes, Multi-Layer Perceptron (MLP), and Random Forest. Our result shows that the best classification accuracy is achieved by Random Forest method. Its accuracy reached 93.15%. It can be concluded that smart phones can be used as an earthquake detector.",
keywords = "earthquake, machine learning, n-gram, signal processing",
author = "{Fikri Aji}, Alham and Putra, {I. Putu Edy Suardiyana} and Petrus Mursanto and Setiadi Yazid",
year = "2014",
doi = "10.1109/SysCon.2014.6819238",
language = "English",
isbn = "9781479920877",
series = "8th Annual IEEE International Systems Conference, SysCon 2014 - Proceedings",
publisher = "IEEE Computer Society",
pages = "72--77",
booktitle = "8th Annual IEEE International Systems Conference, SysCon 2014 - Proceedings",
address = "United States",
note = "8th Annual IEEE International Systems Conference, SysCon 2014 ; Conference date: 31-03-2014 Through 03-04-2014",
}