Wavelet signal processing is broadly used for analysis of real time seismic signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our paper aims to solve and evaluating the frequencies-energy characteristic of earthquake. The wavelet method by Continuous Wavelet Transform (CWT) is able to clearly and simultaneously of amplitudes and frequency-energy from component between the seismogram which seismic sensor broadband recorded in the January 16, 2017 in Medan, North Sumatra. Finally, from machine learning python with morlet wavelet allows good time resolution for high frequencies, and good frequency resolution for low frequencies.

Graphene was synthesized through a two-step pyrolysis method using waste candlenut (Aleurites moluccanus) shells as the precursor. Cerium (Ce)/graphene composites were prepared via an impregnation technique. The resulting graphene and Ce/graphene were characterized using various analytical methods, including Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM-EDS), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscopy (TEM), Thermo Gravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, Cyclic Voltammetry (CV), and Linear Sweep Voltammetry (LSV). The bio-carbon produced predominantly exhibited a graphene structure with flat carbon morphology and an interlayer distance of 0.33 nm. This structural information is supported by XRD data, which shows a broad and weak peak at 2θ = 26° corresponding to the C (002) plane, indicative of graphene presence. FTIR, XPS, and Raman spectroscopy further confirmed the presence of graphene through the detection of Csp2 aromatic bonds and the characteristic D, G, and 2D peaks. Notably, the performance of cerium can be enhanced by the incorporation of graphene, attributed to the large surface area and chemical interactions between Ce and graphene. Consequently, candlenut-derived graphene shows potential as a supportive material for modifying the properties of cerium, due to the current value of Ce/Graphene increase with presence of graphene, thereby opening avenues for various advanced applications, such as sustainable and high-performance energy storage systems.

Machine learning of nuclear explosion signal waveform is core component to realize the characteristics of signal. The processing of waveform explosion signal is broadly used for analysis of real time explosion signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our paper aims to generate the performance of nuclear explosion signal and processing the waveform from seismic sensor by using Continuous Wavelet Transform (CWT). CWT is also clearly to identify of spectral amplitudes and frequency-energy from component of signal seismic performed by Broadband Network in Indonesia. Finally, by machine learning python allows good time resolution for identified and performed of seismic signal from broadband which deployed in Indonesia.

The high pulse voltage low power supply can be built with fly-back transformer as a converter. Generation by using fly-back converter is a significant method which can be improved the circuits of the device to make it simpler, and occupies a smaller space, and cheaper. The study aimed to study and develop a device that will obtain a high pulse voltage low power supply. The results of the study obtained are, the high pulse voltage has reached 50 kV at the frequency of 100 kHz. In addition, when the high pulse voltage is fed to the plane parallel, then at the distance of 50 cm there is a spark, which means it is can generate high pulsed electric fields, it was 50kV/cm.

The observation of earthquake monitoring station had deployed in Indonesia. The data for this study were earthquake data recorded from BMKG Seismic Network. We use the 2016 Aceh earthquake, struck the Indonesia Island of Sumatra with Mw of 6.5 in Aceh province on December 7th 2016, at 05:03 WIB (22:03 UTC December 6th 2016) with duration 14 days from December 1st 2016, 00:00:00 UTC to December 14th 2016 00:00:00 UTC. Spectral analysis response to determine the level of background noise in the seismic station by using the Power Spectral Density and the Probability of Density function from the spectral result. The results of our background noise analysis are useful for characterizing the performance of existing BMKG Network Station, for detecting operational problem.

The turbulence can be expressed as small-scale, short term and frequent change to the velocity of air. The turbulence forecasting in the atmospheric boundary layer is very influenced by gradient Richardson number values. In this case, we use the data set were retrieved from Indonesia Meteorology, Climatology and Geophysics in synoptic hour 0000H. In particular, our methods will use Richardson number value and machine learning approaches by using a support vector machine to forecast the Richardson number value and identification the stability of the layer based on the turbulence forecasting. The results will be practically beneficial as utilities can use the predicted values to generate an adequate amount of Richardson number value to avoid grid outages as well as construct dynamic pricing schemes based upon future turbulence.