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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL)
Martarizal
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics Other

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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; Soekirno, Santoso; Martarizal; Handoko, Djati
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026): In Progress
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.
Co-Authors Ahmad Kadarisman Benyamin Heryanto Rusanto Eko Priyatno Handoko, Djati Hanif Andi Nugraha Imam Fachruddin Santoso Soekirno Soekirno, Santoso Supriyatna