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All Journal Media Ilmu Keolahragaan Indonesia ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JSSF : Journal of Sport Science and Fitness International Journal of Applied Sciences and Smart Technologies
Prapto Nugroho
Jurusan Ilmu Keolahragaan Universitas Negeri Semarang
Computer Science & IT Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering

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Broad-Band Noise Reduction using Least Mean Square (LMS)-Adaptive Line Enhancer (ALE) on Doppler Blood Flow Sound Signal SANTOSO, DANIEL; WAHYUNGGORO, OYAS; NUGROHO, PRAPTO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.189

Abstract

ABSTRAKHaemorrhoidal artery ligation (HAL) adalah metode operasi wasir yang memiliki keunggulan rasa sakit minimal dan waktu pemulihan cepat. Prosedur HAL bergantung pada bunyi Doppler untuk menentukan letak arteri dalam liang anus. Derau yang tercampur dalam sinyal membuat dokter kesulitan mendengarkan bunyi Doppler yang menandakan letak arteri wasir. Adaptive line enhancer (ALE) dengan algoritma least mean square (LMS) digunakan dalam penelitian ini untuk mengurangi aras derau yang terkandung dalam sinyal bunyi Doppler aliran darah. Hasil simulasi menunjukkan parameter mean square error (MSE), signal to noise ratio (SNR), dan waktu eksekusi untuk berbagai orde tapis. Nilai maksimum power spectral density (PSD) sinyal yang masuk ke tapis -86 dB/Hz, kemudian turun menjadi -101 dB/Hz setelah lewat tapis. SNR tertinggi 40,03 dB didapat ketika orde tapis 16. Waktu eksekusi hampir tidak berubah meskipun orde filter dinaikkan.Kata kunci: pengurangan derau, bunyi Doppler, ALE, LMS, HAL ABSTRACTHemorrhoidal artery ligation (HAL) is one of surgical methods in treating hemorrhoids disease known for less pain and fast recovery. HAL procedure relies on the Doppler audible sound to locate arteries within rectal column. Noise interfering the signal of interest makes arteries detection becomes difficult task for the surgeon since signal and noise spectral overlaps. Adaptive line enhancer (ALE) with least mean square (LMS) algorithm is used in this study to reduce noise level contained in Doppler blood flow sound signal. The simulation results show the mean square error (MSE), signal to noise ratio (SNR), and execution time for different filter order. The maximum power spectral density (PSD) value of the noisy signal and filtered signal are -86 dB/Hz and -101 dB/Hz. respectively. The highest SNR of 40.03 dB is obtained when filter tap order is set to 16. However signal processing time remains almost unchanged as the filter tap order increased.Keywords: noise reduction, Doppler sound, ALE, LMS, HAL
Improving the Accuracy of Prediction of Dissolved Oxygen and Nitrate Level Using LSTM with K-Means Clustering and Spearman Analysis Arshella, Ika Arva; Mustika, I Wayan; Nugroho, Prapto
International Journal of Applied Sciences and Smart Technologies Volume 07, Issue 2, December 2025
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v7i2.12361

Abstract

This study discusses how to prepare data properly before entering the learning process for prediction using Deep Learning (DL). Long Short-Term Memory (LSTM) is one of the DL methods that is often used for prediction because of its superiority in maintaining long-term information. Although LSTM has proven effective, there are issues related to low-quality data that can reduce prediction accuracy. This problem is important to discuss because accuracy is important in predicting a value while field conditions can reduce the quality of the data taken. Data merging based on the relationship of each data collection location using the Spearman analysis and the K-Means clustering method is used to improve data quality. The results of the study show that improving data quality by merging data using K-Means has been successfully applied to various dataset conditions. In this study, we used two types of datasets related to river water quality, namely Dissolved Oxygen (DO) concentration and Nitrate levels for our simulation. The first data set produced DO predictions for eight locations with an average R2 = 0.9998, MAE = 0.0007, MSE = 1,13×10-6. The second data set produced nitrate predictions for ten locations with an average R2 = 0.7337, MAE = 0.0111, MSE = 0,00029
Co-Authors Arshella, Ika Arva Daniel Santoso I Wayan Mustika Oyas Wahyunggoro Susilo, Agus Joko Taufiq Hidayah Yulianto, Muchamad Arif