Indonesian Journal of Electrical Engineering and Computer Science Vol. 31, No. 1, July 2023, pp. 238~247 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v31.i1.pp238-247  238 Journal homepage: http://ijeecs.iaescore.com Development of an adaptive finite impulse response filter optimization algorithm using rough set theory Aaron Don M. Africa, John Arvin Mercado, Joshua Kenichi Sim Department of Electronics and Computer Engineering, De La Salle University, Manila, Philippines Article Info ABSTRACT Article history: Received Dec 7, 2022 Revised Mar 16, 2023 Accepted Mar 23, 2023 Signal processing is crucial that as one sends information, there is a corresponding process to encode, decode, and clean the signal of unwanted noise and disruptions via use of filters. Due to the environment and how unpredictable it can be and how noise can come from almost anywhere, the typical filter to be used are adaptive filters. Adaptive filters are non-linear filters and have been used regularly regarding adaptive signal processing, this means that the filter changes accordingly and adapts to the environmental noise surrounding it. The world today has numerous applications for adaptive filters such as channel equalization and acoustic noise cancellation. This incentivizes the further development of this specific technology and the constant research that is ongoing. The main component when it comes to adaptive filtering is the algorithms used for the filter. This research compares the least mean square (LMS) and recursive least square (RLS) algorithms concerning their effectiveness in filtering out unwanted acoustic noises. The paper will cover the design and implementation of an optimized rough set based adaptive finite impulse response (FIR) filter for acoustic noise cancellation. The microstrip and bowtie antenna were used to relay the data. The software MATLAB was used for the simulation. Keywords: Bowtie antenna Finite impulse response Least mean square Microstrip antenna Recursive least square Rough set theory Signal processing This is an open access article under the CC BY-SA license. Corresponding Author: Aaron Don M. Africa Department of Electronics and Computer Engineering, De La Salle University 2401 Taft Ave., Malate, Manila 1004, Philippines Email: aaron.africa@dlsu.edu.ph 1. INTRODUCTION Digital signal processing has been a major component of any form of communications system all around the world [1], [2]. Moving with the times as the world progresses, so does the technology of signal processing. A non-linear filter known as the adaptive filter has had a major role in the field of digital signal processing [3]-[5]. This is due to the filter’s capability to adapt in real time depending on the changing noisy environment that surrounds it [6]. This means that as the input signal changes, so does the process of noise attenuation [7], [8]. When talking about adaptive signal processing the component that dictates the changes are the algorithms it uses such as the least mean square (LMS) and recursive least square (RLS) algorithms, These filters have numerous applications, one of the most common is in the study of acoustic noise cancellation. There are plenty of advantages when making use of this specific filter, the main concern however is the complexity and length of said filter [9]. Due to its computational complexity, the system becomes less ideal in applications due to concerns in terms of the speed at which it can operate. With numerous applications of adaptive filters, studies are now primarily concerned with the improvement of the speed and efficiency of these forms of filters [10]. These are being done by manipulating the individual components of the software such as the algorithm, and the hardware itself.