DOI: https://doi.org/10.14710/jtsiskom.7.4.2019.166-171

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

Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia

Received: 15 Jul 2019; Revised: 19 Sep 2019; Accepted: 7 Oct 2019; Available online: 12 Oct 2019; Published: 31 Oct 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
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%.
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Keywords: accelerometer; earthquake; earthquake detection; support vector machine
Funding: Sekolah Tinggi Meteorologi Klimatologi dan Geofisika, Indonesia

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Section: Original Research Articles
Language : ID
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  1. H. Salahuddin, “Bencana gempabumi,” in DRR Action Plan Workshop: Strengthened Indonesian Resilience: Reducing Risk from Disasters, Yogyakarta, Indonesia, Jan. 2016. doi: 10.13140/RG.2.1.1112.6808
  2. M. A. Massinai, K. R. Amaliah, L. Lantu, Virman, and M. F. Ismullah, “Analisis percepatan tanah maksimum, kecepatan tanah maksimum dan mmi di wilayah Sulawesi Utara,” in Seminar Nasional Fisika SNF-UNJ, Jakarta, Indonesia, Oct. 2016, pp. 33-36. doi: 10.21009/0305020407
  3. R. M. Taruna, S. Rohadi, A. Rudyanto, and D. T. Heryanto, “Penentuan ground motion prediction equations (GMPEs) dengan metode euclidean dan likelihood untuk wilayah Jawa Timur,” Jurnal Meteorologi dan Geofisika, vol. 17, no. 3, pp. 177-189, 2016
  4. C. Sulaeman and A. Cipta, “Model intensitas gempa bumi di Maluku Utara,” Jurnal Lingkungan dan Bencana Geologi, vol. 3 no. 2, pp. 79-88, 2012
  5. W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Device-free human activity recognition using commercial wifi devices,” IEEE Journal on Selected Areas in Communications, vol. 35 no. 5, pp. 1118-1131, 2017. doi: 10.1109/JSAC.2017.2679658
  6. O. C. Ann and L. B. Theng, “Human activity recognition: a review,” in IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, Nov. 2014, pp. 389-393. doi: 10.1109/ICCSCE.2014.7072750
  7. S. A. Ronao and S. B. Cho, “Human activity recognition with smartphone sensors using deep learning neural networks,” Expert System With Application, vol. 59, pp. 235-244, 2016. doi: 10.1016/j.eswa.2016.04.032
  8. F. Attal, S. Mohammed, M. Dedabrishvilli, F. Chamroukhi, L. Oukhellou, and Y. Amirat, "Physical human activity recognition using wearable sensors," Sensors, vol. 15, no. 12, pp. 31314–31338, 2015. doi: 10.3390/s151229858
  9. F. J. Ordonez, and D. Roggen, “Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition,” Sensors, vol. 16, no. 1, pp. 1-25, 2016. doi: 10.3390/s16010115
  10. A. R. Musthafa and H. Tjandrasa, “Kombinasi sinyal EEG dan giroskop untuk kendali mobil virtual dengan menggunakan modifikasi ICA dan SVM,” Jurnal Buana Informatika, vol. 7, no. 3, pp. 169-178, 2016. doi: 10.24002/jbi.v7i3.655
  11. L. H. Ochoa, L. F. Nino, and C. A. Vargas, “Fast determination of earthquake depth using seismic records of a single station, implementing machine learning techniques,” Ingeniería e Investigación, vol. 38, no. 2, pp. 91-103, 2018. doi: 10.15446/ing.investig.v38n2.68407
  12. D. Anguita, A. Ghio, and L. Oneto, “A public domain dataset for human activity recognition using smartphones,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, Apr. 2013, pp. 437-442
  13. A. Perdana, M. T. Furqon, and Indriati, “Penerapan algoritma support vector machine (SVM) pada pengklasifikasian penyakit kejiwaan skizofreni,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, pp. 3162-3167, 2018
  14. M. Hassouna, A. Tarhini, T. Elyas, and M. S. A. Trab, “Customer churn in mobile markets: a comparison of techniques,” International Business Research, vol. 8, no. 8, pp. 224-237, 2015. doi: 10.5539/ibr.v8n6p224

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