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"Telah dilakukan analisis metode dekomposisi spektral berbasis transformasi wavelet kontinyu (CWT) terintegrasi atribut seismik Amplitudo RMS dan Similarity dalam mendelineasikan zona patahan-rekahan didukung dengan analisis data sumur dan log FMI (FullboreFormation Micro Imager) dalam menentukan arah patahan-rekahan. Daerah penelitian ini berada pada Lapangan "Falah", Cekungan Jawa Timur dengan formasi Tuban berumur Miosen. Objek penelitian dikategorikan batuan karbonat jenis reef built up dan zona menarik untuk dianalisis pada reservoar karbonat yaitu berupa zona patahan dan rekahan. Hasilnya metode dekomposisi spekral berbasis CWT dapat memperlihatkan patahan-rekahan pada frekuensi tinggi 40 Hz dan terintegrasi Atribut seismik Amplitudo RMS pada lebar jendela 10 ms dan Similarity pada 25 ms. Patahan-rekahan memiliki arah umum kemiringan sebesar 700 berarah timurlaut-baratdaya. Ketiga atribut yang digunakan pada penelitian ini dapat mendelineasikan arah patahan dan rekahan pada reservoar karbonat reef built up.

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There have been done analysis of spectral decomposition method which was based on Continuous Wavelet Transformation (CWT), integrated Seismic Attributes of RMS amplitude and Similarity. To delineate fault-fracture zone is supported with well data analysis and FMI (FullboreFormation Micro Imager) log is used to define fault-fracture direction. This project research is located at “Falah” field. East Java basin with Tuban formation is in Miocene era. Research object is categorized carbonate rock with reef built up type and the zone is interesting to analyze of carbonate reservoir which are fault and fracture zone.

Result of spectral decomposition method which was based on CWT can show fault-fracture in high frequency at 40Hz and integrated seismic attribute of RMS amplitude with window width at 10ms and then similarity at 25ms. Fault-fracture has common dip at 70° of North East – South West direction. Three attributes were used in this research can delineate fault and fracture direction of carbonate reservoir with reef built up type."

"Pada beberapa orang difabel mengalami kesulitan pada saat bergerak dalam aktivitas sehari-harinya. Penggunaan prostetik dapat mengurangi keterbatasan tersebut. Pada penggunaan prostetik dapat dimodifikasi dengan alat bantu gerak (aktuator) yang dikendalikan oleh brain computer interface (BCI) guna mengontrol prostetik dengan gelombang otak. Aktivitas membayangkan melakukan gerak motorik yang disebut motor imagery (MI) apabila dapat di-recognition dapat memudahkan pada difabel untuk mengendalikan prostetik miliknya. Tulisan ini bertujuan untuk menjelaskan bagaimana me-recognition sinyal elektroensefalografi (EEG) dengan mencoba mengklasifikasikan sinyal MI EEG. Simulasi dilakukan pada bahasa Python pada framework Tensorflow, Keras. Jenis machine learning yang dipilih adalah Convolutional Neural Network (CNN). Dataset diperoleh dari PhysioNet.org, diolah dengan metode Continuous Wavelet Transformation (CWT) dengan library MNE.

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Some people with disabilities have trouble doing their daily activities. Prosthetics could reduce the difficulties to some degree. The use of a prosthetic can be modified by the addition of an actuator (generate of motion) driven by BCI (brain computer interface) to control prosthetic by brain waves. If we could make the recognition of the brain wave in imaginary activities of motoric movement called motor imagery (MI), it would help people with disabilities to better control their prosthetics. This article’s aim to describe how to do the recognition of EEG signals (electroencephalography) by trying to classify the MI EEG signals. The simulation was run in Phyton on a Tensorflow framework, with a keras wrapper. Convolutional Neutral Network (CNN) was chosen in this research as the machine learning. The datasets gathered from PhysioNet.org were transformed using the library MNE with the Continuous Wavelet Transformation (CWT) method."