Wavelet based machine learning approach for spectral seismic signal analysis: A case study North Tapanuli earthquake

Sinambela, Marzuki; Tarigan, Kerista; Humaidi, Syahrul; Situmorang, Manihar

doi:10.1063/5.0003129

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…A regularly used complicated wavelet in CWT analysis is the Morlet wavelet, which is used during this study as its periodic sinusoidal form capacity that wavelet scales can very fairly be approximated in terms of Hz frequency and interpretation can, therefore, be linked to the existing giant physique of work in seismology based on Fourier frequencies [18], [19].…”

Section: Methodsmentioning

confidence: 99%

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Sinambela¹,

Situmorang²,

Tarigan³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.

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Section: Methodsmentioning

confidence: 99%

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Sinambela¹,

Situmorang²,

Tarigan³

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Pengertian tersebut dapat disimpulkan bahwa kinerja sumber daya manusia adalah prestasi kerja atau hasil kerja baik kualitas maupun kuantitas yang dicapai sumber daya manusia persatuan periode waktu dalam melaksanakan tugas kerjanya sesuai dengan tanggung jawab yang diberikan kepadanya. Menurut (Sinambela, Tarigan, Humaidi, & Situmorang, 2020) sangatlah sulit untuk menetapkan suatu definisi kinerja yang dapat memberikan pengertian yang komprehensif. Penggunaan kata kinerja sendiri pun kadang-kadang disamaartikan dengan prestasi kerja, efektivitas kerja, hasil kerja, pencapaian tujuan, produktivitas kerja, dan berbagai istilah lainnya.…”

Section: Kinerja Karyawanunclassified

“…Sesungguhnya sekalipun ada persamaan pengertian kinerja dengan berbagai istilah tersebut, akan tetapi terdapat perbedaan pengertian dasarnya maupun prosesnya. Menurut Amstrong dan Baron dalam (Sinambela et al, 2020), performance sering diartikan sebagai turunan dari terjemahan Bahasa Inggris sebagai kinerja. Kinerja sering diartikan sebagai hasil kerja atau prestasi kerja, meskipun sesungguhnya kinerja bermakna lebih luas, sebab kinerja bukan saja berbicara hasil kerja akan tetapi juga termasuk di dalamnya proses berlangsungnya.…”

Section: Kinerja Karyawanunclassified

Analisis Work Motivation dan Work Environment terhadap Employee Performance (Studi pada Pks Aek Torop di Labuhanbatu Selatan)

Gultom

Marpaung

2022

JOSR

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Pada masa pandemi yang berdampak buruk pada sektor ekonomi dunia saat ini, pembangunan sumber daya manusia merupakan hal yang penting dan krusial. Pasalnya, di dalam pengembangan sumber daya manusia tersebut terdapat pembentukan personal yang kualitasnya baik dalam hal keterampilan, loyalitas kerja, hingga kemampuan individu dalam bekerja. Perusahaan yang memiliki kualitas sumber daya manusia yang baik, akan menjadi aset penting bagi perusahaan pendorong keberhasilan pencapaian tujuan perusahaan dan akan berdampak pada performa serta kemajuan perusahaan, sehingga pengembangan sumber daya manusia harus selalu dilakukan dan diprioritaskan guna meningkatkan kinerja di dalam perusahaan. Tujuan dari penelitian ini adalah 1) Mengetahui seberapa besar pengaruh Work Motivation terhadap Employee Performance, 2) Mengetahui seberapa besar pengaruh work environment terhadap employee performance, 3) Mengetahui pengaruh work motivation dan work environment terhadap employee performance. Penelitian ini menggunakan metode penelitian kuantitatif dengan pendekatan asosiatif. Metode pengumpulan data adalah dengan menyebarkan kuesioner. Jumlah sampel yang diteliti adalah 60 orang. Metode yang digunakan dalam penelitian ini adalah uji validitas, uji asumsi klasik, uji regresi linear berganda, uji hipotesis, uji koefisien determinasi. Proses perhitungan data menggunakan software statistic yakni SPSS for windows versi v 22. Hasil penelitian ini menunjukkan bahwa work motivation (X1) berpengaruh positif dan signifikan terhadap employee performance yang dibuktikan dengan hasil thitung (2,296) > ttabel (2,2002). Work environment (X2) berpengaruh positif dan signifikan terhadap employee performance yang dibuktikan dengan hasil thitung (2,788) > ttabel (2,2002). Work motivation dan work environment secara bersama-sama berpengaruh positif dan signifikan terhadap employee performance dibuktikan dengan hasil uji signifikansi simultan Fhitung (12,779) > Ftabel (3,156). Pada perhitungan koefisien determinasi diperoleh hasil 0,310 yang artinya bahwa variabel independen mempengaruhi variabel dependen sebesar 31%, sedangkan sisanya dipengaruhi oleh variabel lain

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“…In particular, we propose to explore through wavelets micro‐to‐macro interactions at points where we have material or load discontinuities, such as tips (in the former case) or concentrated forces (in the latter). We follow a view introduced in Bosi 20 and Bosi et al 21 to detect mechanical discontinuities through the analysis of wavelet spikes, a view investigated further in other studies 22–24 and references therein.…”

Section: Introductionmentioning

confidence: 99%

Micro‐to‐macro interactions in complex bodies: Wavelet‐based post‐processing detection

Bosi¹,

Mariano

Salvatori

2021

Math Methods in App Sciences

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We propose to couple finite element simulations and wavelet‐based post‐processing analysis to explore deeply the interaction degree of microscopic events on the gross behavior of complex bodies (which class includes metamaterials), above all around material or load discontinuities. After summarizing the theoretical structures our procedure refers to, as a sample case we select a special class of complex materials, namely, quasicrystals, and show how the proposed scheme works. As a result, we point out the effects of atomic rearrangements characterizing the quasicrystal structure on the stress field around a crack tip in static and dynamical setting. Our procedure can be also used for the analysis of dynamic experimental data. In this case, it allows us to detect discontinuities at least along directions selected within the body. In turn, our procedure can be used for monitoring purposes.

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Wavelet based machine learning approach for spectral seismic signal analysis: A case study North Tapanuli earthquake

Cited by 5 publications

References 12 publications

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

Analisis Work Motivation dan Work Environment terhadap Employee Performance (Studi pada Pks Aek Torop di Labuhanbatu Selatan)

Micro‐to‐macro interactions in complex bodies: Wavelet‐based post‐processing detection

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