Klasifikasi percepatan dari sinyal gempa bumi dan sinyal linier aktivitas manusia menggunakan akselerometer smartphone berbasis algoritme support vector machine

Acceleration classification of earthquake signals and linear signals of human activity using smartphone accelerometer based on support vector machine algorithm

*Hapsoro Agung Nugroho orcid scopus  -  Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia
Haryas Subyantara Wicaksana  -  Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia
Received: 15 Jul 2019; Revised: 19 Sep 2019; Accepted: 7 Oct 2019; Published: 31 Oct 2019; Available online: 12 Oct 2019.
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Citation Format:
The threat of earthquake calamity spread throughout most of the Indonesian archipelago. Smartphone’s accelerometer usage as a seismic parameter detector in Indonesia, of which the noise has obstacles, mainly due to human activities. This study aims to classify linear acceleration signals caused by human activity and earthquake acceleration signals as an initial effort to reduce noise caused by human activity in the smartphone’s accelerometer signals. Both signals are classified by using the Support Vector Machine (SVM) algorithm of which consists of several steps, respectively, data collection, data preprocessing, data segmentation, feature extraction, and classification. These algorithms are tested to 2545 human activity signals in trouser pocket, 2430 human activity signals in shirt pocket and earthquake acceleration signals. Based on the test results by using the confusion matrix, linear acceleration signal data caused by human activity and earthquake acceleration signals can be classified properly using an SVM algorithm with Polynomial or Gaussian kernel with a small kernel scale value. The algorithms can achieve an accuracy of 87.74% to 97.94%.
Keywords: accelerometer; earthquake; earthquake detection; support vector machine
Funding: Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia

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