Optimasi Filter Bandpass Aktif untuk Sinyal Lemah Menggunakan Quantum Particle Swarm Optimization pada Studi Kasus Spektral Daya Sinyal Electroencephalogram
DOI:
https://doi.org/10.62048/qjms.v1i2.46Keywords:
Quantum Particle Swarm Optimization, Optimization, Average Power, Band Pass Filter, ElectroencephalogramAbstract
This research focuses on using Quantum Particle Swarm Optimization to optimize active bandpass filters for weak signals. Electroencephalograph signal data obtained from other researchers was used as a case study in this research. This Electroencephalogram signal data, consisting of 1280 signal amplitudes with a sampling frequency of 256 Hz, was characterized before being fed into Quantum Particle Swarm Optimization for optimization. The optimization goal of Quantum Particle Swarm Optimization is to achieve a signal frequency range with a maximum average power value. For bandpass filter design, the frequency range obtained from Quantum Particle Swarm Optimization is used as a reference. The frequency range of 9.9 Hz to 13 Hz and 15.99 Hz to 30 Hz provides optimal conditions. The filter design is based on the frequency range of optimization results and component values R1, R2, and R3 respectively 5.1M?, 10.2 M?, and 1M? with C1=C2=0.01?F for frequencies 9.9 Hz to 13 Hz, and a value of 1 .2M?, 2.4 M?, and 1M? each with C1=C2=0.01?F for frequencies 15.99 Hz to 30 Hz. From the research that has been conducted, Quantum Particle Swarm Optimization is effectively able to optimize the active bandpass filter design process in the case study of electroencephalogram signals.
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