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All Journal Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Semnastek JUITA : Jurnal Informatika ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Teknologi dan Sistem Komputer Jurnal Meteorologi Klimatologi dan Geofisika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Indonesian Physical Review Social, Humanities, and Educational Studies (SHEs): Conference Series Scientific Journal of Informatics Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Hapsoro Agung Nugroho
Sekolah Tinggi Meteorologi Klimatologi Dan Geofisika
Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Journal : Jurnal Teknologi dan Sistem Komputer

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Klasifikasi percepatan dari sinyal gempa bumi dan sinyal linier aktivitas manusia menggunakan akselerometer smartphone berbasis algoritme support vector machine Hapsoro Agung Nugroho; Haryas Subyantara Wicaksana
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 4, Year 2019 (October 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.7.4.2019.166-171

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%.
Co-Authors Agustina Rachmawardani Ahmad Ghozali Akram, Rafi Syah Aries Kristianto Arno, Giarno Benyamin Kusumoputro Dedi Sucahyono Deni Septiadi Hairatunnisa Hairatunnisa Hamdi, Azhari Haris Al Handi Sulistyo Widodo, Handi Sulistyo Hariyanto Hariyanto Hariyanto Haryas Subyantara Wicaksana Haryas Subyantara Wicaksana Hudayat, Nurul JUFRI, RAVYDO ANGGARA Mahmud Yusuf Margaretta S, Chinthya Martha, Agustya Adi Marzuki Sinambela Maulana Aliva, Muhammad Reza Moh. Mambaul Ulum Mustarang, Alhusen Mustarang, Alhusen NUGRAHEINI, IMMA REDHA Nugraheni, Imma Redha Nurul Hidayat Prakoso, Tio Azhar Radjab, Fachri RAMDHANI, ERICH PUTRA Relly Margiono Rista Hernandi Virgianto RUSANTO, BENYAMIN HERYANTO Sastra K. Wijaya Satrio, Adji Siti Hasanah Soegiarto, Ita Soekirno, Santoso Suharni Suharni Sukatmo, Sukatmo Sutejo, Bayu Wicaksana, Haryas Subyantara