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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Spektra: Jurnal Fisika dan Aplikasinya Jurnal ULTIMA Computing PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL)
Martarizal
Department Of Physics Faculty Of Mathematics And Natural Sciences, Universitas Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics Other

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Analysis of Noise Removal Performance in Speech Signals through Comparison of Median Filter, Low FIR Filter, and Butterworth Filter: Simulation and Evaluation Putri, Nurulita Purnama; ., Martarizal
Ultima Computing : Jurnal Sistem Komputer Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3678

Abstract

This research aims to analyze the performance of three types of filters, namely median filters, low FIR filters, and Butterworth filters, in eliminating noise in sound signals. Evaluation is carried out through simulation and evaluation using the Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR) parameters. The simulation results show that the three filters are able to produce signal estimates that are close to the original signal with low MSE values. The median filter shows the best performance with an MSE of 0.015833 and the highest SNR of 51.6334 dB, indicating its ability to reduce noise without sacrificing signal clarity. FIR and Butterworth filters also provide good results, although with slightly lower levels of accuracy. In conclusion, median filters are the optimal choice for noise removal in speech signals, while FIR and Butterworth filters remain good alternatives depending on application requirements. Further research and practical testing are needed for validation in real-world situations
Analysis of Noise Removal Performance in Speech Signals through Comparison of Median Filter, Low FIR Filter, and Butterworth Filter: Simulation and Evaluation: Median filter; FIR low filter; Butterworth filter; noise removal; signal noise; Mean Squared Error (MSE); Signal-to-Noise Ratio (SNR);simulation; evaluation Putri, Nurulita Purnama; ., Martarizal
ULTIMA Computing Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3678

Abstract

This research aims to analyze the performance of three types of filters, namely median filters, low FIR filters, and Butterworth filters, in eliminating noise in sound signals. Evaluation is carried out through simulation and evaluation using the Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR) parameters. The simulation results show that the three filters are able to produce signal estimates that are close to the original signal with low MSE values. The median filter shows the best performance with an MSE of 0.015833 and the highest SNR of 51.6334 dB, indicating its ability to reduce noise without sacrificing signal clarity. FIR and Butterworth filters also provide good results, although with slightly lower levels of accuracy. In conclusion, median filters are the optimal choice for noise removal in speech signals, while FIR and Butterworth filters remain good alternatives depending on application requirements. Further research and practical testing are needed for validation in real-world situations
Hybrid DAE-GAN Model with U-Net Architecture for Seismic Signal Denoising Eko Priyatno; Ahmad Kadarisman; Santoso Soekirno; Martarizal
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA08

Abstract

Seismic data is important for geophysical studies, but it often faces interferences that complicate the analysis of underground structures. This research introduces a new method using deep learning to reduce noise in seismic recordings. It combines a Denoising Autoencoder (DAE) with a Generative Adversarial Network (GAN). In this method, a U-Net model serves as the Generator to create a noise-free signal from the contaminated input. A CNN-based Discriminator distinguishes between the generated and original signals. The Generator's loss function includes Mean Squared Error (MSE) for accuracy and Adversarial Loss for realistic features. The model was trained on the STEAD dataset and its performance evaluated with measures like Signal-to-Noise Ratio (SNR), RMSE, and PRD. Results show that this model improves SNR and produces a clean signal similar to the original both visually and spectrally. This approach could enhance automation and efficiency in preprocessing seismic data.
Early Detection of Seismic Signal Anomalies Using Raspberry Pi 5 and Lightweight Machine Learning Models Ahmad Kadarisman; Imam Fachruddin; Santoso Soekirno; Hanif Andi Nugraha; Benyamin Heryanto Rusanto; Martarizal
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA14

Abstract

Data integrity is crucial for seismic monitoring systems, but is often compromised by anthropogenic or instrumental anomalies. This paper proposes a lightweight edge computing framework using Raspberry Pi 5 for real-time anomaly detection. MiniSEED data from the high-noise TOJI station were processed through segmentation, statistical or spectral feature extraction, and unsupervised models (isolation forest and autoencoder). The results show a detection latency of 78-113 ms with minimal resource consumption (<35% CPU, <200 MB RAM) and 82% correlation with ground-truth anomalies. This framework can be used on networked seismographs with limited resources such as those of the BMKG.
Flatline Anomaly Detection in Automatic Weather Station Air Temperature Sensor Data Using LSTM Autoencoder Supriyatna; Santoso Soekirno; Martarizal; Djati Handoko
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14486

Abstract

The quality of air temperature data from Automatic Weather Stations (AWS) is crucial for meteorological analysis, climatology, and early warning systems. However, flatline anomalies, a condition where sensor values ​​tend to remain constant over a period of time, can degrade data quality and are often not optimally detected by conventional rule-based quality control (QC) methods. Previous research is also limited in specifically examining flatline detection, with most studies focusing on general anomalies and not integrating deep learning approaches with operational quality control systems. This study proposes a data-driven approach using a Long Short-Term Memory Autoencoder (LSTM-AE) combined with Level-1 QC. The novelty of this study lies in the use of a normal-only training scheme, anomaly threshold determination based on the reconstruction error distribution, and post-detection diagnosis to identify flatline characteristics. The methods include QC filtering, sliding window formation, model training, threshold determination, and anomaly detection. The results show stable model performance with an anomaly threshold value of 0.01177 (MSE). Of the 985,730 data windows, approximately 0.578% were detected as anomalies, indicating that flatline occurrences are relatively small but still significant to data quality. Most anomalies are short-lived and discontinuous, indicating localized sensor noise. This study demonstrates that LSTM-AE is effective as an adaptive flatline detection method and has the potential to be implemented as an automated QC module in AWS systems to improve data reliability.
Edge Seismic Denoising: Benchmarking the Hailo-8L on Raspberry Pi 5 Antonia Indriyani Juniar; Naura Anbiyya Faka Mursaid; Ahmad Kadarisman; Martarizal
Spektra: Jurnal Fisika dan Aplikasinya Vol. 11 No. 2 (2026): SPEKTRA: Jurnal Fisika dan Aplikasinya, Volume 11 Issue 2, August 2026
Publisher : Program Studi Fisika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/SPEKTRA.112.04

Abstract

Real-time seismic monitoring in urban and remote environments is increasingly challenged by strong anthropogenic noise and limited computational resources at field stations. Although deep learning-based seismic denoising methods have demonstrated promising performance, their deployment on low-power edge AI platforms for continuous onsite monitoring remains insufficiently explored. This study investigates the feasibility of implementing a Conv2D-based Denoising Autoencoder (DAE) for real-time seismic signal denoising on a Raspberry Pi 5 integrated with the Hailo-8L AI accelerator. The model was trained using synthetic noise injection on seismic waveform data from the CIKJI station to generate noisy-clean signal pairs. The deployment pipeline included signal preprocessing, Conv1D-to-Conv2D adaptation, ONNX conversion, and INT8 quantization into the Hailo Execution Format (HEF). Experimental evaluation on 21,598 seismic windows demonstrated that the Hailo INT8 implementation achieved a Pearson correlation of 0.9645 and an SNR of 14.95 dB, compared to 0.9727 and 17.08 dB obtained by CPU FP32 inference, respectively. Despite a modest degradation in denoising accuracy, the Hailo-8L significantly reduced CPU utilization from 61.4% to 14.5% and maintained stable sub-millisecond inference latency during 24-hour simulated streaming tests. These results demonstrate that edge AI accelerators provide a practical tradeoff between denoising performance and computational efficiency, supporting the development of low-power real-time seismic monitoring systems for onsite deployment in resource-constrained environments. This study provides the first empirical characterization of NPU-accelerated seismic denoising under continuous real-time streaming conditions, establishing practical benchmarks for edge AI deployment in resource-constrained seismic monitoring networks.
Co-Authors Ahmad Kadarisman Ahmad Kadarisman Antonia Indriyani Juniar Benyamin Heryanto Rusanto Eko Priyatno Handoko, Djati Hanif Andi Nugraha Imam Fachruddin Naura Anbiyya Faka Mursaid Putri, Nurulita Purnama Santoso Soekirno Santoso Soekirno Supriyatna