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p-Index From 2021 - 2026
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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Media of Computer Science
Vugar Abdullayev
Azerbaijan State Oil and Industry University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Evaluating the effectiveness of digital filtering techniques in electronic stethoscopes: a study of Kalman and Butterworth filters Endang Dian Setioningsih; Sumber Sumber; Triwiyanto Triwiyanto; Vugar Abdullayev; Farid Amrinsani; Bima Maulana Raharjo; Tetrik Fa’altin
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.8674

Abstract

Heart sound, or phonocardiogram (PCG), signals are often distorted by noise from respiration, movement, and the surrounding environment, which complicates accurate cardiac feature extraction in portable monitoring systems. This study aims to design and evaluate an effective digital filtering method to enhance PCG signal quality obtained from a low-cost acquisition system based on a condenser microphone sensor and an ESP32 microcontroller. The main contribution of this work is the implementation and comparison of two noise-reduction approaches: Butterworth band-pass filters of various orders and the Kalman filter applied to PCG signals acquired from mannequin-based simulations. Data were recorded for 10 seconds at a 1000 Hz sampling rate, processed in MATLAB, and analyzed using the fast Fourier transform (FFT) to determine the optimal frequency ranges. Experimental results demonstrate that the 8th-order Butterworth band-pass filter achieved the highest signal-to-noise ratio (SNR) improvement, averaging 25.659 dB, outperforming other configurations. These findings indicate that an appropriately tuned Butterworth filter provides a simpler yet robust solution for real-time PCG denoising in embedded systems. Future work will integrate the filtering process directly into the ESP32 firmware and evaluate its performance on human subjects to enhance clinical applicability.
Preprocessing Image for License Plate Detection: A Systematic Literature Review Riyan Bagas Dwi Prasetyo; Vugar Abdullayev; Nurcahya Pradana Taufik Prakisya; Yudianto Sujana; Rahmat Siswanto
Media of Computer Science Vol. 2 No. 2 (2025): December 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i2.241

Abstract

Rapid population growth contributes to an increase in the volume of vehicles, creating major challenges in their management. One potential solution is the application of deep learning-based artificial intelligence technology for automatic detection of vehicle license plates. This research uses a Systematic Literature Review (SLR) approach to evaluate the performance of various deep learning architectures in the detection process. Out of 125 articles identified, 20 articles were selected based on specific selection criteria. The analysis revealed that preprocessing techniques, such as HE, AHE, ECHE, CLAHE, and ECLACHE, have significant contributions in the processing of vehicle license plate datasets. These techniques were able to improve the visual quality of the images, thus supporting the detection process with an accuracy rate of more than 95%. This research also identified challenges, such as high computational requirements and large-scale data processing. Further research is recommended to apply preprocessing on standardized datasets to develop a reliable, efficient and sustainable detection system.
Development process of decision support systems using data mining technology Bahar Asgarova; Elvin Jafarov; Nicat Babayev; Allahshukur Ahmadzada; Vugar Abdullayev; Triwiyanto Triwiyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp703-714

Abstract

Decision support systems (DSS) play a pivotal role as computerized tools, guiding and enhancing decision-making processes vital for organizational progress. This research focuses on developing a system tailored for dynamic decision-making, particularly emphasizing the integration of data mining technology. Decision algorithms and neural networks are discussed in depth, providing a comprehensive understanding of the analytical tools crucial for effective decision support. Additionally, the research sheds light on potential risks, ensuring a nuanced view of challenges that may impact the development of DSS. A significant portion of the study is dedicated to the design of DSS architecture and the strategic integration of data mining within the database. The proposed development stages for a business information system, ranging from feasibility study to release, serve as a structured framework for practical implementation. Details within each stage, including data analysis, cleaning, and module development, are meticulously examined. Emphasis is placed on critical steps such as system design, database design, and extract, transform, load (ETL) process design, elucidating their importance in the holistic development of DSS. The conclusion reinforces the paramount importance of leveraging data mining technology in the process of developing decision support systems.
Co-Authors Allahshukur Ahmadzada Bahar Asgarova Bima Maulana Raharjo Elvin Jafarov Farid Amrinsani Nicat Babayev Nurcahya Pradana Taufik Prakisya Rahmat Siswanto Riyan Bagas Dwi Prasetyo Setioningsih, Endang Dian Sumber Sumber Tetrik Fa’altin Triwiyanto Triwiyanto Triwiyanto Triwiyanto Yudianto Sujana